Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-05-10T16:38:45.648151Z
Paper Citation Record · LEDGER
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos
As of 22 July 2026, this Paper Citation Record lists 45 of 45 outbound references and 1 inbound Pith citation observation for arXiv:2604.09819.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-07-21T06:31:05.380196+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links, observed 2026-05-09T14:10:37.533748Z
A source-named dated measurement, never combined with another source.
Source: pith, observed 2026-05-11T17:01:09.708632Z
Reference resolution
45 of 45 outbound references displayed
- verified exact5
- verified fuzzy40
- unresolved0
- parse uncertain0
- malformed identifier0
- metadata mismatch0
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation fb100018-7027-407e-86c3-efeeadabf3bb · outbound
This paper cites an unresolved cited work.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Unresolved cited work
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:46.075180Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation 1de3a579-9418-48bb-9d0c-3d030b01f8e6 · outbound
This paper cites Traffic accident detection video dataset for ai-driven computer vision systems in smart city transportation.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Traffic accident detection video dataset for ai-driven computer vision systems in smart city transportation
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:45.964896Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation 80f8bc50-4735-431d-953c-4fdba5c38f72 · outbound
This paper cites Collision detection: An improved deep learning approach using senet and resnext.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Collision detection: An improved deep learning approach using senet and resnext
Reference 3
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:45.968298Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation 18fa89d2-f5da-4fd7-8fce-8e9923ecca41 · outbound
This paper cites Collaborative learning of anomalies with privacy (clap) for unsupervised video anomaly detection: A new baseline.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Collaborative learning of anomalies with privacy (clap) for unsupervised video anomaly detection: A new baseline
Reference 4
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:45.971420Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation 00e3b79b-1a71-4f5d-9e79-4c978e84dacf · outbound
This paper cites Idda: A large-scale multi-domain dataset for autonomous driving.IEEE Robotics and Automation Letters, 5(4):5526–5533.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Idda: A large-scale multi-domain dataset for autonomous driving.IEEE Robotics and Automation Letters, 5(4):5526–5533
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:45.974628Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation dc62327b-d0d5-4e40-8b89-43f583e91610 · outbound
This paper cites A kernel multiple change-point algorithm via model selection.Jour- nal of machine learning research, 20(162):1–56.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos A kernel multiple change-point algorithm via model selection.Jour- nal of machine learning research, 20(162):1–56
Reference 6
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:45.980474Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation e69f5767-ccf9-4a93-973a-642c99320309 · outbound
This paper cites Qwen2.5-VL Technical Report.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Qwen2.5-VL Technical Report
Reference 7
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T08:26:00.664582Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation cba817ba-5485-4553-a7cc-65f4585b7a7d · outbound
This paper cites Uncertainty-based traffic accident anticipation with spatio-temporal relational learn- ing.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Uncertainty-based traffic accident anticipation with spatio-temporal relational learn- ing
Reference 8
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:45.986544Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation 0bbe9ad1-7897-4863-ad8f-aa4b3e957408 · outbound
This paper cites Drive: Deep reinforced accident anticipation with visual explanation.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Drive: Deep reinforced accident anticipation with visual explanation
Reference 9
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:46.095517Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation 675fe5a2-88b3-4fe7-8e5b-ad3dda07e653 · outbound
This paper cites New efficient algorithms for multiple change- point detection with reproducing kernels.Computational Statistics & Data Analysis, 128:200–220.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos New efficient algorithms for multiple change- point detection with reproducing kernels.Computational Statistics & Data Analysis, 128:200–220
Reference 10
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:46.091873Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation 56fc947f-b2c8-4e9e-9f33-9ce60b537182 · outbound
This paper cites Tads: a novel dataset for road traffic acci- dent detection from a surveillance perspective.The Journal of Supercomputing, 80(18):26226–26249.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Tads: a novel dataset for road traffic acci- dent detection from a surveillance perspective.The Journal of Supercomputing, 80(18):26226–26249
Reference 11
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:45.983393Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation b5c259a1-97e8-47a7-96df-c01454bdfa0b · outbound
This paper cites Anticipating accidents in dashcam videos.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Anticipating accidents in dashcam videos
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:46.087339Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation 68bc3227-7f41-4327-a4c2-74bcb1a58d2b · outbound
This paper cites Molmo and pixmo: Open weights and open data for state-of-the-art vision-language models.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Molmo and pixmo: Open weights and open data for state-of-the-art vision-language models
Reference 13
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:46.080019Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation cff649f8-8293-4d19-b32b-31e8600b0228 · outbound
This paper cites Trafficvlm: A controllable visual lan- guage model for traffic video captioning.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Trafficvlm: A controllable visual lan- guage model for traffic video captioning
Reference 14
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:46.068293Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation d2698400-88f5-4cf9-b585-e79556731c69 · outbound
This paper cites Dada-2000: Can driving accident be pre- dicted by driver attention? analyzed by a benchmark.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Dada-2000: Can driving accident be pre- dicted by driver attention? analyzed by a benchmark
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:46.071902Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation a410b087-1f58-4e0f-8974-3eb1e8ddd934 · outbound
This paper cites Vision-based traffic accident detection and anticipation: A survey.IEEE Transactions on Circuits and Systems for Video Technology, 34(4):1983–1999.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Vision-based traffic accident detection and anticipation: A survey.IEEE Transactions on Circuits and Systems for Video Technology, 34(4):1983–1999
Reference 16
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:46.057333Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation 85af0d54-1acb-4cfe-97a1-9809f1b8ecdc · outbound
This paper cites Two-frame motion estimation based on polynomial expansion.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Two-frame motion estimation based on polynomial expansion
Reference 17
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:46.064586Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation 29276072-7d61-435d-8013-fa22a4e71d1b · outbound
This paper cites Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection
Reference 18
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:46.061129Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation 95439c61-3cc8-4bf1-9e56-ff64528b90c3 · outbound
This paper cites Open source computer vision library.https:// github.com/itseez/opencv.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Open source computer vision library.https:// github.com/itseez/opencv
Reference 19
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:46.083781Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation d92776c3-a750-444c-9041-5cc470142dcd · outbound
This paper cites Ultralytics yolo11.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Ultralytics yolo11
Reference 20
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:46.104668Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation 928afb03-9503-4bba-a56e-577eff6fe3d1 · outbound
This paper cites Crash to not crash: Learn to identify dangerous vehicles using a simulator.Proceedings of the AAAI Conference on Artificial Intelligence, 33(01):978–985.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Crash to not crash: Learn to identify dangerous vehicles using a simulator.Proceedings of the AAAI Conference on Artificial Intelligence, 33(01):978–985
Reference 21
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:46.046294Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation 9c2108a6-f24d-4fd1-982b-f7ac506ae37e · outbound
This paper cites V2x-sim: Multi-agent col- laborative perception dataset and benchmark for autonomous driving.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos V2x-sim: Multi-agent col- laborative perception dataset and benchmark for autonomous driving
Reference 22
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:46.042218Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation 3c14ac1d-d425-4727-9068-b718ed392b2f · outbound
This paper cites Fu- ture frame prediction for anomaly detection–a new baseline.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Fu- ture frame prediction for anomaly detection–a new baseline
Reference 23
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:46.053736Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation a106d653-df04-4726-ad32-9ed629716f35 · outbound
This paper cites A hybrid video anomaly detection frame- work via memory-augmented flow reconstruction and flow- guided frame prediction.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos A hybrid video anomaly detection frame- work via memory-augmented flow reconstruction and flow- guided frame prediction
Reference 24
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:46.031623Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation 047f2d58-95bd-486d-b2c6-1734e655956c · outbound
This paper cites A simulation-based frame- work for urban traffic accident detection.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos A simulation-based frame- work for urban traffic accident detection
Reference 25
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:46.035139Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation 13b0ef91-63c7-44cd-bb12-36c3409e02cf · outbound
This paper cites Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Anomaly Detection in Video Using Predictive Convolutional Long Short-Term Memory Networks
Reference 26
- Resolution
- verified exact
- arxiv_id, observed 2026-05-11T08:26:00.672829Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation b17a3519-8494-4c95-a0c6-213148a29a33 · outbound
This paper cites DINOv2: Learning Robust Visual Features without Supervision.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos DINOv2: Learning Robust Visual Features without Supervision
Reference 27
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T08:26:00.682615Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation 4b86b3e5-0022-4843-89f0-02b5a6fdd782 · outbound
This paper cites World Health Organization.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos World Health Organization
Reference 28
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:46.027987Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation 9aecd972-032b-4e3d-acbd-d9f042a9f974 · outbound
This paper cites Zero-shot hazard identification in autonomous driving: A case study on the coool benchmark.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Zero-shot hazard identification in autonomous driving: A case study on the coool benchmark
Reference 29
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:46.038862Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation 0ebbacb5-d6e2-45ed-86b6-bc583e8078bb · outbound
This paper cites The golden hour in trauma: dogma or medical folk- lore?Injury, 46(4):525–527.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos The golden hour in trauma: dogma or medical folk- lore?Injury, 46(4):525–527
Reference 30
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:46.049977Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation 4fbaac32-98ec-4e69-8677-df3b949bfa0b · outbound
This paper cites Cadp: A novel dataset for cctv traffic camera based accident analysis.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Cadp: A novel dataset for cctv traffic camera based accident analysis
Reference 31
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:46.101080Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation af13ceea-5fa6-43ab-98a5-57842aa75d29 · outbound
This paper cites Hand, and Kostas Alexis.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Hand, and Kostas Alexis
Reference 32
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:46.013385Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation 0103ef64-8fad-4751-b87a-63f79977c57a · outbound
This paper cites Synthetic datasets for au- tonomous driving: A survey.IEEE Transactions on Intel- ligent Vehicles, 9(1):1847–1864.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Synthetic datasets for au- tonomous driving: A survey.IEEE Transactions on Intel- ligent Vehicles, 9(1):1847–1864
Reference 33
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:46.021183Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation 85e90307-0e35-4bc9-9136-8c25b0c28ff5 · outbound
This paper cites Real-world anomaly detection in surveillance videos.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Real-world anomaly detection in surveillance videos
Reference 34
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:46.007125Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation 33fc3c98-a95b-47d5-ab6a-c52e562c933c · outbound
This paper cites Shift: a synthetic driving dataset for continuous multi-task domain adaptation.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Shift: a synthetic driving dataset for continuous multi-task domain adaptation
Reference 35
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:46.010408Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation 8923ef4e-62a2-4a82-a5b4-d138fb5fb9d4 · outbound
This paper cites Selec- tive review of offline change point detection methods.Signal Processing, 167:107299.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Selec- tive review of offline change point detection methods.Signal Processing, 167:107299
Reference 36
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:46.017207Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation a57918bf-da2d-400c-b3dd-287f1e4c45bc · outbound
This paper cites SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos SigLIP 2: Multilingual Vision-Language Encoders with Improved Semantic Understanding, Localization, and Dense Features
Reference 37
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T08:26:00.687155Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation f4f8f25d-7824-4ef5-ae75-60cbe68ef226 · outbound
This paper cites Detection of road ac- cidents using synthetically generated multi-perspective acci- dent videos.IEEE Transactions on Intelligent Transporta- tion Systems, 24(2):1926–1935.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Detection of road ac- cidents using synthetically generated multi-perspective acci- dent videos.IEEE Transactions on Intelligent Transporta- tion Systems, 24(2):1926–1935
Reference 38
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:46.024745Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation c6e13fc5-a63f-4bb8-9720-7f50e3126c54 · outbound
This paper cites Deepaccident: A motion and accident prediction bench- mark for v2x autonomous driving.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Deepaccident: A motion and accident prediction bench- mark for v2x autonomous driving
Reference 39
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:45.992986Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation 5ae997b0-fabf-4936-9bfb-be119661e705 · outbound
This paper cites Opv2v: An open benchmark dataset and fusion pipeline for perception with vehicle-to-vehicle communica- tion.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Opv2v: An open benchmark dataset and fusion pipeline for perception with vehicle-to-vehicle communica- tion
Reference 40
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:45.989739Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation f0e7e7a6-6463-4130-bc2b-1376a3e2d28c · outbound
This paper cites Tad: A large-scale benchmark for traffic accidents detection from video surveillance.IEEE Access, 13:2018–2033.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Tad: A large-scale benchmark for traffic accidents detection from video surveillance.IEEE Access, 13:2018–2033
Reference 41
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:45.996221Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation 8d9faf49-01ad-4925-a05b-8415b413c0db · outbound
This paper cites Crandall, and Ella M.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Crandall, and Ella M
Reference 42
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:45.977498Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation 3ef81c43-c629-41e7-9548-dbd946b1e57e · outbound
This paper cites VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos VideoLLaMA 3: Frontier Multimodal Foundation Models for Image and Video Understanding
Reference 43
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T08:26:00.677973Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation 470d1b20-4235-4125-a6ad-ecdd8c089b31 · outbound
This paper cites When language and vision meet road safety: leveraging multimodal large language models for video-based traffic accident analysis.Accident Analysis & Prevention, 219:108077.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos When language and vision meet road safety: leveraging multimodal large language models for video-based traffic accident analysis.Accident Analysis & Prevention, 219:108077
Reference 44
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:45.999680Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Observation f674a749-e9cc-4a9a-8a37-e9343a3554c0 · outbound
This paper cites Towards vi- sion zero: The tum traffic accid3nd dataset.
ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos Towards vi- sion zero: The tum traffic accid3nd dataset
Reference 45
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-05-17T14:19:46.003374Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.
Pith citing papers
Observation a8c90125-24e2-4ef6-88e2-681fd943e1fb · inbound
Two-Pass Zero-Shot Temporal-Spatial Grounding of Rare Traffic Events in Surveillance Video cites this paper.
Two-Pass Zero-Shot Temporal-Spatial Grounding of Rare Traffic Events in Surveillance Video ACCIDENT: A Benchmark Dataset for Vehicle Accident Detection from Traffic Surveillance Videos
Reference 13
- Resolution
- verified exact
- local_arxiv, observed 2026-05-11T17:01:09.711279Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-07-21T06:31:05.380196+00:00.