Real-Time Anomaly Detection in Video Streams | Paper Citation Record

Observation e2d74489-7fa5-4d5f-8a3b-bc37a34dbfb9 · outbound

This paper cites Variational autoencoder based anomaly detection using reconstruction probability.

Real-Time Anomaly Detection in Video Streams Variational autoencoder based anomaly detection using reconstruction probability

Reference 1

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Observation f7b37c1e-042d-4e3f-bfc1-0ffa76d78467 · outbound

This paper cites 3D-CNN-Based Fused Feature Maps with LSTM Applied to Action Recognition.

Real-Time Anomaly Detection in Video Streams 3D-CNN-Based Fused Feature Maps with LSTM Applied to Action Recognition

Reference 2

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Observation 7e111955-cf12-443c-a64d-e2bf8f4c44a4 · outbound

This paper cites ViViT: A Video Vision Transformer.

Real-Time Anomaly Detection in Video Streams ViViT: A Video Vision Transformer

Reference 3

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Observation 7b7d054e-d5c3-4b3f-9103-37aa83b89937 · outbound

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Real-Time Anomaly Detection in Video Streams Unresolved cited work

Reference 4

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Observation 677da17b-4443-4a60-af77-35a9069e4aae · outbound

This paper cites Understanding the role of individual units in a deep neural network.

Real-Time Anomaly Detection in Video Streams Understanding the role of individual units in a deep neural network

Reference 5

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Observation a24ef515-0c78-439a-b4ed-3760b8cb590c · outbound

This paper cites YOLOv4: Optimal Speed and Accuracy of Object Detection.

Real-Time Anomaly Detection in Video Streams YOLOv4: Optimal Speed and Accuracy of Object Detection

Reference 6

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Observation cec741fe-262c-4fc2-a3ac-25348d543e07 · outbound

This paper cites Utilizing Amari-Alpha Divergence to Stabilize the Training of Generative Adversarial Networks.

Real-Time Anomaly Detection in Video Streams Utilizing Amari-Alpha Divergence to Stabilize the Training of Generative Adversarial Networks

Reference 7

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Observation 91526204-2a7e-42fa-a633-5fa87c933a68 · outbound

This paper cites Emerging Properties in Self-Supervised Vision Transformers.

Real-Time Anomaly Detection in Video Streams Emerging Properties in Self-Supervised Vision Transformers

Reference 8

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Observation c69c23c4-5475-4998-9d24-0d6551d7e3e9 · outbound

This paper cites Chakraborty, A.

Real-Time Anomaly Detection in Video Streams Chakraborty, A

Reference 9

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Observation 98aff70f-def6-46bb-9403-3bdd39cf9fa5 · outbound

This paper cites Anomaly detection: A survey.

Real-Time Anomaly Detection in Video Streams Anomaly detection: A survey

Reference 10

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Observation 03d7a60e-d39a-4e6f-8a0c-d13d4a651c03 · outbound

This paper cites Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks.

Real-Time Anomaly Detection in Video Streams Grad-CAM++: Generalized Gradient-Based Visual Explanations for Deep Convolutional Networks

Reference 11

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Observation 795e7812-8687-434f-9d88-b959370ef2b4 · outbound

This paper cites You Only Look One-level Feature.

Real-Time Anomaly Detection in Video Streams You Only Look One-level Feature

Reference 12

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Observation fe94b54c-5a21-4250-bd31-572dc7f5ac7f · outbound

This paper cites Autoencoder-based network anomaly detection.

Real-Time Anomaly Detection in Video Streams Autoencoder-based network anomaly detection

Reference 13

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Observation b841bd63-7612-4a58-a3ad-18b0626bcde0 · outbound

This paper cites Xception: Deep Learning with Depthwise Separable Convolu- tions.

Real-Time Anomaly Detection in Video Streams Xception: Deep Learning with Depthwise Separable Convolu- tions

Reference 14

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Observation 3583bcf4-58e8-4036-8b4a-6b55381f536e · outbound

This paper cites Chollet et al.

Real-Time Anomaly Detection in Video Streams Chollet et al

Reference 15

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Observation cccefeca-848c-4785-8c9e-23eb45d094b7 · outbound

This paper cites Abnormal Event Detection in Videos us- ing Spatiotemporal Autoencoder.

Real-Time Anomaly Detection in Video Streams Abnormal Event Detection in Videos us- ing Spatiotemporal Autoencoder

Reference 16

Resolution: verified exact; doi, observed 2026-08-12T05:59:46.322995Z

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Observation e34a927f-d8c4-4a91-9d67-8b854b823de5 · outbound

This paper cites Residual spatiotemporal autoencoder for unsupervised video anomaly detection.

Real-Time Anomaly Detection in Video Streams Residual spatiotemporal autoencoder for unsupervised video anomaly detection

Reference 17

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Observation 446e15c9-dec5-4fac-8be3-7711387a397b · outbound

This paper cites Adversarial autoencoders for anomalous event detection in images.

Real-Time Anomaly Detection in Video Streams Adversarial autoencoders for anomalous event detection in images

Reference 18

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Observation 7fe0faad-d1ca-4214-a189-72521fc8a6fa · outbound

This paper cites Continual Learning for Anomaly Detection in Surveillance Videos.

Real-Time Anomaly Detection in Video Streams Continual Learning for Anomaly Detection in Surveillance Videos

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-12T05:59:46.020448Z

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Observation e1854a42-7961-476f-b9c2-5f92175b0038 · outbound

This paper cites An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale.

Real-Time Anomaly Detection in Video Streams An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale

Reference 20

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Observation 984f9a79-3149-42c8-ac37-9ca1041592e5 · outbound

This paper cites Finding Structure in Time.

Real-Time Anomaly Detection in Video Streams Finding Structure in Time

Reference 21

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Observation e06a32be-727f-4c60-92e3-e835a2998bdf · outbound

This paper cites You only look at one sequence: Rethinking transformer in vision through object detection.

Real-Time Anomaly Detection in Video Streams You only look at one sequence: Rethinking transformer in vision through object detection

Reference 22

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Observation 680486f3-17f9-4916-bbfb-0c0e4c5d8e62 · outbound

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Real-Time Anomaly Detection in Video Streams Unresolved cited work

Reference 23

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Observation 4c032852-e4a4-483c-849a-c0216b52cf6f · outbound

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Real-Time Anomaly Detection in Video Streams Unresolved cited work

Reference 24

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Observation 93f8206d-301e-493b-8ad2-47fdcc3143c4 · outbound

This paper cites an unresolved cited work.

Real-Time Anomaly Detection in Video Streams Unresolved cited work

Reference 25

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Observation d402ddbf-e79c-4acf-b980-2d423d6b1e22 · outbound

This paper cites YOLOX: Exceeding YOLO Series in 2021.

Real-Time Anomaly Detection in Video Streams YOLOX: Exceeding YOLO Series in 2021

Reference 26

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Observation 53ef940e-2564-44c6-bd23-e6641bce3791 · outbound

This paper cites Learning to Forget: Contin- ual Prediction with LSTM.

Real-Time Anomaly Detection in Video Streams Learning to Forget: Contin- ual Prediction with LSTM

Reference 27

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Observation c9f6fc5f-00c1-4d09-91a1-a2c3d51e0ea2 · outbound

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Real-Time Anomaly Detection in Video Streams Unresolved cited work

Reference 28

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Observation 175f00cf-947b-4b81-ae8d-843d075b03a6 · outbound

This paper cites Fast R-CNN.

Real-Time Anomaly Detection in Video Streams Fast R-CNN

Reference 29

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Observation b529506a-78e0-49b7-bbf8-3e29a6ca7914 · outbound

This paper cites Rich Feature Hierar- chies for Accurate Object Detection and Semantic Segmentation.

Real-Time Anomaly Detection in Video Streams Rich Feature Hierar- chies for Accurate Object Detection and Semantic Segmentation

Reference 30

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Observation cf59f3c4-3cf0-44b4-bd9f-d91dfb5cb7d2 · outbound

This paper cites Generative Adversarial Networks.

Real-Time Anomaly Detection in Video Streams Generative Adversarial Networks

Reference 31

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Observation 10e6c070-9dac-47e3-9f73-dc00411de05b · outbound

This paper cites M3d-CAM: A PyTorch library to generate 3D data attention maps for medical deep learning.

Real-Time Anomaly Detection in Video Streams M3d-CAM: A PyTorch library to generate 3D data attention maps for medical deep learning

Reference 32

Resolution: verified exact; local_arxiv, observed 2026-08-12T05:59:46.478199Z

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Observation fdc35153-0cd0-4e90-8e24-6214ae9e6831 · outbound

This paper cites Learning Temporal Regularity in Video Sequences.

Real-Time Anomaly Detection in Video Streams Learning Temporal Regularity in Video Sequences

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:47.028567Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a25b125d-afb1-4010-81da-91f4521e19b9 · outbound

This paper cites Escaping the Big Data Paradigm with Compact Transformers.

Real-Time Anomaly Detection in Video Streams Escaping the Big Data Paradigm with Compact Transformers

Reference 34

Resolution: unresolved; no resolver link, observed 2026-08-12T05:59:46.072480Z

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Observation 67d40e71-70ff-40cf-893c-1bda551b8cb7 · outbound

This paper cites Deep Residual Learning for Image Recognition.

Real-Time Anomaly Detection in Video Streams Deep Residual Learning for Image Recognition

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:47.019403Z

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Observation 8a66e7ff-6905-47b7-9c60-2a83df6b6b24 · outbound

This paper cites Long Short-Term Memory.

Real-Time Anomaly Detection in Video Streams Long Short-Term Memory

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:47.010934Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b9c7da58-6e3b-44d4-86ca-29194796f5b4 · outbound

This paper cites Densely Connected Convolu- tional Networks.

Real-Time Anomaly Detection in Video Streams Densely Connected Convolu- tional Networks

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:47.002409Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1b9ee2bc-29a7-4b01-aa96-48f206179907 · outbound

This paper cites an unresolved cited work.

Real-Time Anomaly Detection in Video Streams Unresolved cited work

Reference 38

Resolution: unresolved; raw_fallback, observed 2026-08-12T05:59:46.994360Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 845f64f0-5de5-4107-9a56-9f43aba1702e · outbound

This paper cites Mobile Neural Architecture Search Net- work and Convolutional Long Short-Term Memory-Based Deep Features Toward Detecting Violence from Video.

Real-Time Anomaly Detection in Video Streams Mobile Neural Architecture Search Net- work and Convolutional Long Short-Term Memory-Based Deep Features Toward Detecting Violence from Video

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.985423Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 83de1841-f64c-44ef-b35c-ec0bea904add · outbound

This paper cites Incremental Training for Image Classification of Unseen Objects.

Real-Time Anomaly Detection in Video Streams Incremental Training for Image Classification of Unseen Objects

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.977059Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3d95640f-5301-438c-8add-58c9ab80cfc9 · outbound

This paper cites 3D Convolutional Neural Networks for Human Action Recognition.

Real-Time Anomaly Detection in Video Streams 3D Convolutional Neural Networks for Human Action Recognition

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.968080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 4538d1d0-0374-42ef-b76e-b4366bec1b47 · outbound

This paper cites LayerCAM: Exploring Hierarchical Class Activation Maps for Localization.

Real-Time Anomaly Detection in Video Streams LayerCAM: Exploring Hierarchical Class Activation Maps for Localization

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.960130Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a994acf1-052c-494b-bc2f-4cae438b6fc2 · outbound

This paper cites Serial Order: A Parallel Distributed Processing Approach.

Real-Time Anomaly Detection in Video Streams Serial Order: A Parallel Distributed Processing Approach

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.951355Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6e01bc3f-0045-4540-bb36-87d5451ac9c6 · outbound

This paper cites Segment Anything.

Real-Time Anomaly Detection in Video Streams Segment Anything

Reference 44

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Observation ea53ac2f-748e-4f34-acea-88107d8ef60f · outbound

This paper cites Kotikalapudi and al.

Real-Time Anomaly Detection in Video Streams Kotikalapudi and al

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.942962Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a0bcee98-f8d2-4c98-a356-3e00822b0a4a · outbound

This paper cites ImageNet classification with deep convolutional neural networks.

Real-Time Anomaly Detection in Video Streams ImageNet classification with deep convolutional neural networks

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.934582Z

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No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 08b13427-4a4a-4d61-8e6f-b819d8dcb7af · outbound

This paper cites Transfer Learning for Illustration Classification.

Real-Time Anomaly Detection in Video Streams Transfer Learning for Illustration Classification

Reference 47

Resolution: verified exact; local_arxiv, observed 2026-08-12T05:59:46.449664Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation ed826d9e-0b8d-44e1-bad9-90cd2d4f9d97 · outbound

This paper cites Temporal Convolutional Networks for Action Segmentation and Detection.

Real-Time Anomaly Detection in Video Streams Temporal Convolutional Networks for Action Segmentation and Detection

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.926012Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0dcafbf9-e5f6-4f42-b6a3-cc4ec4cb714e · outbound

This paper cites Backpropagation Applied to Handwritten Zip Code Recognition.

Real-Time Anomaly Detection in Video Streams Backpropagation Applied to Handwritten Zip Code Recognition

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.917622Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2fa87ff7-6f0b-44b0-9ec6-474382a6eea9 · outbound

This paper cites Learning to detect anomaly events in crowd scenes from synthetic data.

Real-Time Anomaly Detection in Video Streams Learning to detect anomaly events in crowd scenes from synthetic data

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.909773Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 7010b316-03a6-486b-8580-90567fe7dc10 · outbound

This paper cites A Unified Approach to Interpreting Model Predictions.

Real-Time Anomaly Detection in Video Streams A Unified Approach to Interpreting Model Predictions

Reference 51

Resolution: unresolved; no resolver link, observed 2026-08-12T05:59:46.128726Z

Source-reported events for the cited work

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Observation 608b9f93-6b88-4c71-ad7b-dbf2bce80c9a · outbound

This paper cites Remembering history with convolutional LSTM for anomaly detection.

Real-Time Anomaly Detection in Video Streams Remembering history with convolutional LSTM for anomaly detection

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.901392Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation de7c2c57-a191-479d-86fa-4589bfe7111c · outbound

This paper cites A motion-aware ConvLSTM network for action recognition.

Real-Time Anomaly Detection in Video Streams A motion-aware ConvLSTM network for action recognition

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.891799Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 44525c81-091b-4bfa-b8f7-8d67102af10e · outbound

This paper cites Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks.

Real-Time Anomaly Detection in Video Streams Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks

Reference 54

Resolution: unresolved; no resolver link, observed 2026-08-12T05:59:46.137675Z

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Observation 06563113-2a94-46ea-b9fd-585c2ae38336 · outbound

This paper cites A hybrid approach for search and rescue using 3DCNN and PSO.

Real-Time Anomaly Detection in Video Streams A hybrid approach for search and rescue using 3DCNN and PSO

Reference 55

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.883287Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6db3faab-811e-435d-8094-0b40512682eb · outbound

This paper cites an unresolved cited work.

Real-Time Anomaly Detection in Video Streams Unresolved cited work

Reference 56

Resolution: unresolved; raw_fallback, observed 2026-08-12T05:59:46.874848Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5a288aca-0733-4ab8-bc5a-539893d57e63 · outbound

This paper cites Real-Time Video Anomaly Detection for Smart Surveillance.

Real-Time Anomaly Detection in Video Streams Real-Time Video Anomaly Detection for Smart Surveillance

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.866678Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c38940e2-67f4-442f-a301-3a4e670c6228 · outbound

This paper cites Feature Visualization.

Real-Time Anomaly Detection in Video Streams Feature Visualization

Reference 58

Resolution: malformed identifier; raw_fallback, observed 2026-08-12T05:59:46.856360Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 271238ce-4e1f-422c-920a-4e67dc54d852 · outbound

This paper cites The Building Blocks of Interpretability.

Real-Time Anomaly Detection in Video Streams The Building Blocks of Interpretability

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.847320Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a5892175-56dd-4713-bc06-c0d9f3618d51 · outbound

This paper cites Temporal Fusion Approach for Video Classification with Convolutional and LSTM Neural Networks Applied to Violence Detection.

Real-Time Anomaly Detection in Video Streams Temporal Fusion Approach for Video Classification with Convolutional and LSTM Neural Networks Applied to Violence Detection

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.838877Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0a6e623e-394a-4790-9de8-377a3b120baa · outbound

This paper cites D´ etection d’anomalies en temps r´ eel dans le flux vid´ eo.

Real-Time Anomaly Detection in Video Streams D´ etection d’anomalies en temps r´ eel dans le flux vid´ eo

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.830037Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b7fdd170-f44c-4943-9beb-c29a2f215ee0 · outbound

This paper cites Enhancing Anomaly De- tection in Videos using a Combined YOLO and a VGG GRU Approach.

Real-Time Anomaly Detection in Video Streams Enhancing Anomaly De- tection in Videos using a Combined YOLO and a VGG GRU Approach

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.821922Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 93bc3d6c-66e5-442b-8ae7-8ba4aa8e4e34 · outbound

This paper cites From CNN to CNN + RNN: Adapting Visualization Techniques for Time-Series Anomaly Detection.

Real-Time Anomaly Detection in Video Streams From CNN to CNN + RNN: Adapting Visualization Techniques for Time-Series Anomaly Detection

Reference 63

Resolution: verified exact; local_arxiv, observed 2026-08-12T05:59:46.419875Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation bda68b3c-6506-416b-98a1-c9aa9be5e8d7 · outbound

This paper cites Exploring Convolutional Recurrent architectures for anomaly detection in videos: a comparative study.

Real-Time Anomaly Detection in Video Streams Exploring Convolutional Recurrent architectures for anomaly detection in videos: a comparative study

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.813464Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 91a9fa77-2b4e-4fe5-a594-0f4179d9fc7d · outbound

This paper cites You Only Look Once: Unified, Real-Time Object Detection.

Real-Time Anomaly Detection in Video Streams You Only Look Once: Unified, Real-Time Object Detection

Reference 65

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.805761Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation aa9a9dfc-2515-47c2-bcfe-1e1ac79e92b4 · outbound

This paper cites YOLO9000: Better, Faster, Stronger.

Real-Time Anomaly Detection in Video Streams YOLO9000: Better, Faster, Stronger

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.797817Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation a0a4c51e-c448-4ba5-8370-ee3145cb98e0 · outbound

This paper cites YOLOv3: An Incremental Improvement.

Real-Time Anomaly Detection in Video Streams YOLOv3: An Incremental Improvement

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-12T05:59:46.178673Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 216b40b2-d9b3-4047-b56e-aabee50de855 · outbound

This paper cites an unresolved cited work.

Real-Time Anomaly Detection in Video Streams Unresolved cited work

Reference 68

Resolution: unresolved; raw_fallback, observed 2026-08-12T05:59:46.788314Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 0e3a446d-3332-4462-ad0d-7fa2d26d9a16 · outbound

This paper cites Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks.

Real-Time Anomaly Detection in Video Streams Faster R-CNN: Towards Real- Time Object Detection with Region Proposal Networks

Reference 69

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.779656Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation b8fd0eff-930e-4b28-9ea4-69adb447713a · outbound

This paper cites A study of deep convolu- tional auto-encoders for anomaly detection in videos.

Real-Time Anomaly Detection in Video Streams A study of deep convolu- tional auto-encoders for anomaly detection in videos

Reference 70

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.770901Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 24ecd597-0dd5-4b4f-a1ec-732d30c1093a · outbound

This paper cites “Why Should I Trust You?.

Real-Time Anomaly Detection in Video Streams “Why Should I Trust You?

Reference 71

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.762172Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c42bf0d3-c650-4bc3-91ae-8b395f9b152a · outbound

This paper cites Learning represen- tations by back-propagating errors.

Real-Time Anomaly Detection in Video Streams Learning represen- tations by back-propagating errors

Reference 72

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.752626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3b41b5af-ac56-4098-b96b-16f88d412b91 · outbound

This paper cites Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery.

Real-Time Anomaly Detection in Video Streams Unsupervised Anomaly Detection with Generative Adversarial Networks to Guide Marker Discovery

Reference 73

Resolution: unresolved; no resolver link, observed 2026-08-12T05:59:46.196899Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 92f3855e-4e81-4cde-9bf3-fa74d77bdde0 · outbound

This paper cites Grad-CAM: Visual Explanations from Deep Networks via Gradient- Based Localization.

Real-Time Anomaly Detection in Video Streams Grad-CAM: Visual Explanations from Deep Networks via Gradient- Based Localization

Reference 74

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.744248Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 6902e78d-7a01-4ec8-a099-3cddfd7b59b6 · outbound

This paper cites Deep Learning for Automatic Violence Detection: Tests on the AIRTLab Dataset.

Real-Time Anomaly Detection in Video Streams Deep Learning for Automatic Violence Detection: Tests on the AIRTLab Dataset

Reference 75

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.736688Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c09a1605-8277-4ed2-8634-7ff38548d03f · outbound

This paper cites an unresolved cited work.

Real-Time Anomaly Detection in Video Streams Unresolved cited work

Reference 76

Resolution: unresolved; raw_fallback, observed 2026-08-12T05:59:46.728769Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 35f84549-7607-41d8-92e5-105bce4bec87 · outbound

This paper cites an unresolved cited work.

Real-Time Anomaly Detection in Video Streams Unresolved cited work

Reference 77

Resolution: unresolved; raw_fallback, observed 2026-08-12T05:59:46.720626Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation eb237fa1-14e5-4d9d-bc9a-5ae556e90dc8 · outbound

This paper cites an unresolved cited work.

Real-Time Anomaly Detection in Video Streams Unresolved cited work

Reference 78

Resolution: unresolved; raw_fallback, observed 2026-08-12T05:59:46.711030Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation e2d10cb6-fa53-446e-a946-76952fab5351 · outbound

This paper cites Convolutional LSTM Network: A Machine Learning Approach for Precip- itation Nowcasting.

Real-Time Anomaly Detection in Video Streams Convolutional LSTM Network: A Machine Learning Approach for Precip- itation Nowcasting

Reference 79

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.699299Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c9966111-1cff-4906-9771-00955c6b2040 · outbound

This paper cites Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps.

Real-Time Anomaly Detection in Video Streams Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

Reference 80

Resolution: unresolved; no resolver link, observed 2026-08-12T05:59:46.218819Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation bbe3bacb-20a7-43f9-b45b-abb8d1a83b93 · outbound

This paper cites Very Deep Convolutional Networks for Large-Scale Image Recognition.

Real-Time Anomaly Detection in Video Streams Very Deep Convolutional Networks for Large-Scale Image Recognition

Reference 81

Resolution: unresolved; no resolver link, observed 2026-08-12T05:59:46.222653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 74d04046-8885-4c17-8dc9-8b7cdfd6e335 · outbound

This paper cites SmoothGrad: removing noise by adding noise.

Real-Time Anomaly Detection in Video Streams SmoothGrad: removing noise by adding noise

Reference 82

Resolution: unresolved; no resolver link, observed 2026-08-12T05:59:46.225846Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c3eb3e24-9e34-4223-aaa0-359d549a1bb6 · outbound

This paper cites L’apprentissage non-supervis´ e et ses contradictions.

Real-Time Anomaly Detection in Video Streams L’apprentissage non-supervis´ e et ses contradictions

Reference 83

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.687530Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 083d7d47-f3e0-4fa1-9e09-b0a634876339 · outbound

This paper cites Real-world anomaly detection in surveillance videos.

Real-Time Anomaly Detection in Video Streams Real-world anomaly detection in surveillance videos

Reference 84

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.677998Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c61e1c79-77b8-4332-89c0-ac1e2011a886 · outbound

This paper cites Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning.

Real-Time Anomaly Detection in Video Streams Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning

Reference 85

Resolution: unresolved; no resolver link, observed 2026-08-12T05:59:46.235685Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ed7826e1-5c90-43a8-9069-a31cd6d7ce74 · outbound

This paper cites Going deeper with convolutions.

Real-Time Anomaly Detection in Video Streams Going deeper with convolutions

Reference 86

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.668543Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 8bc5f1fa-198f-400b-9dad-ca84c1aae9ab · outbound

This paper cites EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks.

Real-Time Anomaly Detection in Video Streams EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks

Reference 87

Resolution: unresolved; no resolver link, observed 2026-08-12T05:59:46.241730Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 20358161-c264-469a-8edc-66e8d0d600e4 · outbound

This paper cites Employing long short-term memory and Facebook prophet model in air temperature forecasting.

Real-Time Anomaly Detection in Video Streams Employing long short-term memory and Facebook prophet model in air temperature forecasting

Reference 88

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.660534Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 61eb9224-155b-418f-83f5-315d0561f636 · outbound

This paper cites Learning Spatiotemporal Features with 3D Convolutional Networks.

Real-Time Anomaly Detection in Video Streams Learning Spatiotemporal Features with 3D Convolutional Networks

Reference 89

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.649461Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation f0c0bb35-1f61-4c9d-850e-4bb04fdab654 · outbound

This paper cites Selective Search for Object Recognition.

Real-Time Anomaly Detection in Video Streams Selective Search for Object Recognition

Reference 90

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.639914Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 1bd49c81-358a-438a-83fe-2f7d831798cc · outbound

This paper cites Attention is all you need.

Real-Time Anomaly Detection in Video Streams Attention is all you need

Reference 91

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.631323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation d618de0c-45ad-4808-88be-f442fe44027f · outbound

This paper cites Rapid object detection using a boosted cas- cade of simple features.

Real-Time Anomaly Detection in Video Streams Rapid object detection using a boosted cas- cade of simple features

Reference 92

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.621720Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 3a46ba16-c2a6-404d-834d-2468142f0970 · outbound

This paper cites A New Approach for Abnormal Human Activities Recognition Based on ConvLSTM Architec- ture.

Real-Time Anomaly Detection in Video Streams A New Approach for Abnormal Human Activities Recognition Based on ConvLSTM Architec- ture

Reference 93

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.613110Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation c6bf9dd5-1eab-4093-8bfa-80ae158e2cb4 · outbound

This paper cites Human Activity Clas- sification Using the 3DCNN Architecture.

Real-Time Anomaly Detection in Video Streams Human Activity Clas- sification Using the 3DCNN Architecture

Reference 94

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.603884Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 2b25af92-daa0-4b97-aba5-552b049c3b31 · outbound

This paper cites Violent Be- havioral Activity Classification Using Artificial Neural Network.

Real-Time Anomaly Detection in Video Streams Violent Be- havioral Activity Classification Using Artificial Neural Network

Reference 95

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.594027Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5ae054a2-77f9-4ef9-ba6d-31fd2053978b · outbound

This paper cites YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors.

Real-Time Anomaly Detection in Video Streams YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

Reference 96

Resolution: unresolved; no resolver link, observed 2026-08-12T05:59:46.267708Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 41d9122a-b048-4ebb-b117-ad8135a7623a · outbound

This paper cites CSPNet: A New Backbone that can Enhance Learning Capability of CNN.

Real-Time Anomaly Detection in Video Streams CSPNet: A New Backbone that can Enhance Learning Capability of CNN

Reference 97

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.585910Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 867ca511-2c23-464b-971c-6f0f2c27ae9c · outbound

This paper cites You Only Learn One Representation: Unified Network for Multiple Tasks.

Real-Time Anomaly Detection in Video Streams You Only Learn One Representation: Unified Network for Multiple Tasks

Reference 98

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.578060Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation cd889e2e-6d4e-4642-afbb-9558dd82c6f3 · outbound

This paper cites Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks.

Real-Time Anomaly Detection in Video Streams Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks

Reference 99

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.563146Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Observation 5f3ea0f4-bff6-4a10-9f08-5abedf9f74e4 · outbound

This paper cites Abnormal Event Detection in Videos Using Hybrid Spatio-Temporal Autoencoder.

Real-Time Anomaly Detection in Video Streams Abnormal Event Detection in Videos Using Hybrid Spatio-Temporal Autoencoder

Reference 100

Resolution: verified fuzzy; raw_fallback, observed 2026-08-12T05:59:46.553874Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers