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REVIEW 4 major objections 4 minor 1 cited by

Crowd Scene Analysis using Deep Learning Techniques

T0 review · 4 major / 4 minor · reviewed 2026-08-15 · deepseek-v4-flash

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Pith's one-line read This thesis claims crowd counting works without any annotated images, using rotation classification plus Sinkhorn density matching in a five-column CNN, and that a VGG19-LSTM flags violent frames one at a time.

desk verdict The paper's central zero-annotation counting claim is contradicted by its own supervised pre-training and ground-truth density loss; without aggregate results or a defined prior, it is not ready for review. read the letter →

arxiv 2505.08834 v2 pith:QC26RSNM submitted 2025-05-13 cs.CV cs.AI

Muhammad Junaid Asif This is my paper · ORCID
classification cs.CVcs.AI
keywords CrowdsceneanalysiscountinganomalydetectionMulti-columnconvolutionalnetworkSelf-supervisedlearningLSTMDensitymapestimationViolence
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This thesis sets out to remove two bottlenecks in crowd analysis: the need for annotated head locations in crowd counting, and the clip-level granularity of crowd violence detection. For counting, it claims a completely self-supervised pipeline — a five-column multi-scale CNN learns crowd features by predicting the rotation angle of image crops, and a second stage freezes those features and trains a density regressor by matching its predicted density map, through Sinkhorn distance, to a prior distribution of natural crowd densities, with no annotated density maps involved; the model is claimed to generalize across the ShanghaiTech and UCF-QNRF datasets. If that claim holds, dense-scene counting becomes trainable from raw surveillance footage at essentially zero annotation cost. For anomaly detection, the thesis claims a spatio-temporal VGG-19-LSTM, with fully connected layers replaced by wide dense residual blocks, that classifies violence at the level of individual frames and outperforms state-of-the-art approaches on the Hockey Fight and SCVD datasets.

What carries the argument

The load-bearing object for counting is the Sinkhorn matching step in stage 2: the predicted density distribution is aligned, via optimal transport, to a 'prior distribution that approximates the statistical characteristics of natural crowds,' and this matching is what is supposed to make the density regressor trainable without annotation. Around it sit the five-column M-CNN — parallel convolutional branches with filters of sizes 3×3, 5×5, 7×7, 9×9, and 11×11 that capture local and global scale — and the rotation-classification head (0°, 90°, 180°, 270°) that supplies the stage-1 pseudo-labels. For anomaly detection the machinery is the modified VGG-19, whose fully connected layers are replaced by wide dense residual blocks, feeding an LSTM that captures motion between frames at times t and t−1, ending in a binary normal-versus-abnormal classification.

What would settle it

Reproduce stage 2 on the ShanghaiTech Part A test set: write down the prior distribution the Sinkhorn step must match to, train the density regressor against that target only, and compute aggregate MAE and MSE over the whole test set. If no concrete prior can be stated from the thesis, or if the predicted counts are no better than a constant-density baseline, the self-supervised counting claim is not settled.

Watch

Extended reading notes

Core claim

The central discovery the thesis argues for is that the supervision signal for crowd counting does not have to come from annotated heads. Stage 1 trains the feature extractor on a rotation-classification pretext task — predicting whether a 112×112 crop was rotated by 0°, 90°, 180°, or 270° — which forces the network to learn crowd-relevant structure from raw images alone. Stage 2 freezes those features, appends two further convolutional blocks, and trains the density regressor by minimizing the Sinkhorn distance (an optimal-transport measure) between the predicted density map and a prior distribution assumed to capture the statistical character of natural crowds, so the prediction is pulled toward a statistical target rather than compared with ground-truth annotations. For anomaly detection, the thesis claims that replacing VGG-19's fully connected layers with wide dense residual blocks and passing the extracted spatial features through an LSTM yields a frame-level binary normal/abnormal classifier that reports F1 scores of 0.86 on the Hockey Fight dataset and 0.90 on the SCVD dataset and claims to outperform other state-of-the-art approaches.

Load-bearing premise

The entire counting pipeline rests on the 'prior distribution' of natural crowd densities that stage 2's Sinkhorn matching targets, but the thesis never defines that distribution's form, its parameters, or how it is estimated (Section 3.2.4); if that target is wrong or arbitrary, the density estimation has nothing principled to match to.

Editorial extensions

If this is right

  • Crowd-counting models could be trained from raw, unannotated surveillance footage, eliminating the manual head-annotation step that currently restricts deployment to new scenes.
  • The five-column design asserts that widening a counting network with more parallel scale-specific filters improves accuracy on occluded scenes, non-uniform density, and scale variation without making the network deeper.
  • A density regressor trained purely by distribution matching would transfer across datasets such as ShanghaiTech and UCF-QNRF without re-annotating for each new scene.
  • Frame-level violence classification lets an operator act on short-duration incidents that clip-level or video-level detectors would smooth away.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • The same two-stage recipe — rotation pretext followed by matching a prediction to a statistical prior — could transfer to other counting problems whose objects have predictable statistics (vehicles, animals, cells), because the prior plays the role that ground-truth density maps play in supervised training.
  • The frame-level anomaly formulation, if it holds up, points toward temporal action segmentation: frame scores from the VGG19-LSTM could be pooled to label intervals of a surveillance clip as normal or violent without clip-level annotations.
  • A careful reader should not assume the strongest reading of 'completely self-supervised': Sections 4.1.2 and 4.2.1 describe supervised pre-training on annotated density maps before the self-supervised stage, so the no-annotation claim is best understood as applying to the counting stage proper unless the author states otherwise.
  • The counting results chapter reports per-image predicted counts and rotation-accuracy curves but no aggregate MAE/MSE over the full ShanghaiTech or UCF-QNRF test sets, despite the abstract promising those metrics; an independent replication would need to supply the aggregate numbers to confirm the headline claim.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 4 minor

Summary. This manuscript, based on an M.Sc. thesis, proposes two systems for crowd scene analysis: a self-supervised multi-column CNN for crowd counting and a VGG19-LSTM model for crowd anomaly detection. The counting pipeline is described as a two-stage method in which stage one uses rotation classification as a pretext task and stage two uses Sinkhorn distribution matching for density estimation; the abstract and contribution list claim that this method is completely self-supervised and does not use a single annotated image. Results are reported on ShanghaiTech and UCF-QNRF, while the anomaly detection model is evaluated on the Hockey Fight and SCVD datasets with precision, recall, and F1-score metrics.

Significance. If the central claim were correct, a crowd counting method that requires no annotated images would be a substantial contribution because it would remove the dominant annotation bottleneck in the field. The paper also proposes a five-branch M-CNN and a VGG19-LSTM architecture with wide dense residual blocks, and it reports frame-level anomaly detection rather than clip-level prediction. However, the central zero-annotation claim is directly contradicted by the manuscript's own training description, the quantitative evaluation is missing the promised aggregate MAE/MSE numbers, and the anomaly detection comparisons with prior state-of-the-art methods are not presented. The paper does not ship code, trained models, or a derivation of the loss functions, so the strengths are mainly the clear problem motivation and the architectural description rather than verified empirical results.

major comments (4)
  1. [Sections 4.1.2, 4.2.1, and 4.2.2(B)] The paper's central claim, stated in the abstract and in Section 1.9.1, is that crowd counting is performed by 'completely self-supervised learning without using a single annotated image.' This is contradicted by the manuscript's own training description. Section 4.1.2 states that 'annotated and unannotated data is used for training purposes' and that the annotated dataset, consisting of crowd photos with manually annotated density maps, is used for pre-training. Section 4.2.1 then describes a supervised pre-training step in which the model is fed images and their ground-truth maps. Section 4.2.2(B) states that the distribution matching objective is to minimize the difference between ground-truth density maps and predicted density maps. Since the pipeline regresses toward manually annotated density maps, either in pre-training or in distribution matching, the zero-annotation claim is not supported. This is a load-bearing point because the paper's main novelty rests on eliminating the annotation bottleneck.
  2. [Section 3.2.4] Stage 2 is described as matching the predicted density distribution to 'a prior distribution that approximates the statistical characteristics of natural crowds' using Sinkhorn matching, but the prior is never defined. No equation specifies the prior's parametric form, how it is estimated, or what data is used to derive it. Likewise, the Sinkhorn objective is not written out, and no algorithm or implementation detail is given. Because the entire density estimation stage is built on matching to this prior, the unspecified prior makes the method non-reproducible and prevents the reader from verifying whether the claimed self-supervised density estimation is even well posed.
  3. [Section 4.5 and Tables 4.1-4.5] Although Section 4.4 defines MAE and MSE, and the abstract claims evaluation on ShanghaiTech and UCF-QNRF, no aggregate MAE or MSE values are reported for any test set. The experimental section only presents per-image ground-truth versus predicted counts for a few selected images, and those numbers include substantial errors, e.g., Table 4.3 Image 3 reports 707 ground truth versus 431.64 predicted, and Table 4.5 reports 975 versus 577. Without aggregate errors or a comparison against existing crowd counting methods on the full test splits, the quantitative evaluation claims in the abstract and in Section 7 are unsupported.
  4. [Sections 6.4 and 6.5] The claim that the anomaly detection model outperforms other state-of-the-art approaches is not supported by any comparative table. Section 6.4 reports only precision (0.91), recall (0.82), and F1-score (0.86) on the Hockey Fight dataset without comparing to prior published results, and Section 6.5 reports one result on SCVD. Additionally, Table 6.2 is mislabeled: the section heading refers to SCVD, the table's first column says 'Hockey Fight Dataset,' and the row also says 'Hockey Fight Dataset,' making the reported SCVD result ambiguous. Without a comparison to existing methods and a clear reporting of the experimental setup, the 'outperform state-of-the-art' statement is not verifiable.
minor comments (4)
  1. [Equations 1 and 2 (Section 4.4)] The notation in the MAE and MSE formulas is inconsistent: y_c is defined as 'number of peoples counted' and the denominator symbols are garbled, while the predicted count is not clearly distinguished from the ground-truth count. Please define y_pred and y_GT explicitly and rewrite the equations with standard notation.
  2. [Tables 4.4 and 4.5] Table 4.4 and Table 4.5 have the same caption, 'Ground Truth vs. Predicted Count for Images with Scale In-variation,' but Table 4.5 appears in the complex-background subsection. The captions should be corrected.
  3. [Table 6.2] The row label in Table 6.2 says 'Hockey Fight Dataset' while the section heading is 'Experimental Results on SCVD.' This typo should be fixed so the reported 0.93/0.92/0.90 result is attributed to the correct dataset.
  4. [Section 3.1.1] The subsection numbering in Chapter 3 is inconsistent: '3.1 Theoretical Background' is followed by '3. Data Augmentation' instead of '3.1.1 Data Augmentation.' This should be corrected throughout the chapter.

Circularity Check

0 steps flagged · score 0.0 of 10

No circular derivation: the counting and anomaly-detection pipelines do not assume their own conclusions, although the zero-annotation counting claim is contradicted by the paper's own supervised pre-training and ground-truth density-map losses.

full rationale

The derivation chain is not circular in the technical sense. Stage 1 of the counting method uses rotation classification with labels generated from the input crops; that pretext task is independent of the crowd count, so it can provide a genuine initialization that is not defined in terms of the final count. Stage 2 matches a predicted density to an unspecified 'prior distribution' via Sinkhorn distance, but no equation in the paper shows the predicted density is defined as the prior or as a fitted parameter, so there is no Eq X = Eq Y reduction. The main problem is that the paper labels its method 'completely self-supervised learning without using a single annotated image' (Section 1.9.1), while Section 4.1.2 states 'Annotated and unannotated data is used for training purposes,' Section 4.2.1 says the model is 'trained on a labeled dataset with the help of an annotated dataset' using 'images and their relevant ground-truth maps,' and Section 4.2.2 says 'The density maps are compared to the ground truth density maps created with Gaussian kernels.' That is a factual contradiction about supervision, not a circular derivation: the prediction is not being defined as its own input, and no fitted parameter is renamed as a prediction. The undefined prior in Section 3.2.4 is a serious reproducibility gap, and the claimed MAE/MSE evaluations on ShanghaiTech and UCF-QNRF are not reported as aggregate numbers in Sections 4.5.1-4.5.2, but these are missing-evidence issues outside the circularity rubric. Citations to M-CNN [13] and ConvLSTM [14,15] are external prior works, not author self-citations carrying a uniqueness theorem. Therefore the circularity score is 0.

Assumptions & free parameters 3 free parameters · 4 assumptions · 1 invented entities

The central counting claim rests on an undefined prior distribution, a rotation pretext task whose transfer is assumed, and a second stage that uses ground-truth density maps. The anomaly detection part assumes ImageNet-to-violence transfer. These are unproven modeling choices rather than derived results.

free parameters (3)
  • M-CNN branch count = 5
    The paper increases M-CNN from 3 to 5 parallel columns; this number is chosen by hand and no ablation or theoretical justification is provided.
  • Rotation pretraining crop size = 112x112
    Selected for the rotation pretext task without analysis of its effect on density estimation or transfer to full-image counting.
  • LSTM hidden units = 256
    Set to 256 for video anomaly detection; no ablation or justification beyond a default size.
assumptions (4)
  • domain assumption Rotation classification on 112x112 crops learns features relevant to crowd density estimation.
    Section 3.2.3 claims the rotation pretext task teaches edge and orientation features that transfer to density estimation, but no evidence or ablation supports this transfer.
  • ad hoc to paper A prior distribution approximating natural crowd statistics exists and is suitable for Sinkhorn matching.
    Section 3.2.4 introduces this prior but never defines its form or estimation, making the counting method dependent on an unspecified modeling assumption.
  • domain assumption Ground-truth density maps are available for the second-stage loss.
    Section 4.2.2 B says the network is trained to minimize the difference between predicted density maps and ground-truth density maps created with Gaussian kernels, contradicting the claimed annotation-free setup.
  • domain assumption ImageNet pretrained VGG19 weights transfer to crowd violence detection.
    Section 6.2 uses pretrained VGG19 unchanged; the transferability to hockey fights and smart-city violence is assumed rather than tested.
invented entities (1)
  • Crowd-density prior distribution for Sinkhorn matching
    purpose: Serves as the target distribution in stage 2 distribution matching for density estimation
    The paper does not specify how this prior is constructed, what distribution family it belongs to, or how its parameters are set. Without this, the claimed self-supervised counting mechanism is unfalsifiable and non-reproducible.

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Cite this review

Pith. "Pith review of Crowd Scene Analysis using Deep Learning Techniques." pith.science (2026). https://pith.science/paper/QC26RSNM

@misc{pith2026250508834,
  author       = {Pith},
  title        = {Pith review of: Crowd Scene Analysis using Deep Learning Techniques},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/QC26RSNM}},
  note         = {Machine review of arXiv:2505.08834}
}
read the original abstract

Our research is focused on two main applications of crowd scene analysis crowd counting and anomaly detection In recent years a large number of researches have been presented in the domain of crowd counting We addressed two main challenges in this domain 1 Deep learning models are datahungry paradigms and always need a large amount of annotated data for the training of algorithm It is timeconsuming and costly task to annotate such large amount of data Selfsupervised training is proposed to deal with this challenge 2 MCNN consists of multicolumns of CNN with different sizes of filters by presenting a novel approach based on a combination of selfsupervised training and MultiColumn CNN This enables the model to learn features at different levels and makes it effective in dealing with challenges of occluded scenes nonuniform density complex backgrounds and scale invariation The proposed model was evaluated on publicly available data sets such as ShanghaiTech and UCFQNRF by means of MAE and MSE A spatiotemporal model based on VGG19 is proposed for crowd anomaly detection addressing challenges like lighting environmental conditions unexpected objects and scalability The model extracts spatial and temporal features allowing it to be generalized to realworld scenes Spatial features are learned using CNN while temporal features are learned using LSTM blocks The model works on binary classification and can detect normal or abnormal behavior The models performance is improved by replacing fully connected layers with dense residual blocks Experiments on the Hockey Fight dataset and SCVD dataset show our models outperform other stateoftheart approaches

Figures

Figures reproduced from arXiv: 2505.08834 by the authors.

Figure 1
Figure 1. Homogenous vs Heterogenous Crowds 1 [PITH_FULL_IMAGE:figures/full_fig_p017_1.png] view at source ↗
Figure 6.1
Figure 6.1. Violent Video: Frames 1 to 20 107 [PITH_FULL_IMAGE:figures/full_fig_p019_6_1.png] view at source ↗
Figure 1
Figure 1. Homogenous vs Heterogenous Crowds [PITH_FULL_IMAGE:figures/full_fig_p023_1.png] view at source ↗
Figures from the paper (65 more)
Figure 1
Figure 1. Figure 1 [PITH_FULL_IMAGE:figures/full_fig_p024_1.png]
Figure 1.2
Figure 1.2. Figure 1.2: Actual vs. Physical Crowds Virtual crowds are continuously monitored and recorded through cameras. While physical crowd requires installation of CCTV cameras for monitoring. It requires a big investment for installation of large set-up of CCTV cameras. Attentive manp…
Figure 1.3
Figure 1.3. Figure 1.3: Major applications of Crowd Scene Analysis [PITH_FULL_IMAGE:figures/full_fig_p026_1_3.png]
Figure 1
Figure 1. Figure 1 [PITH_FULL_IMAGE:figures/full_fig_p027_1.png]
Figure 1.4
Figure 1.4. Figure 1.4: A general picture depicting crowd counting [PITH_FULL_IMAGE:figures/full_fig_p027_1_4.png]
Figure 1.5
Figure 1.5. Figure 1.5: A general picture depicting crowd anomaly detection [PITH_FULL_IMAGE:figures/full_fig_p028_1_5.png]
Figure 1
Figure 1. Figure 1 [PITH_FULL_IMAGE:figures/full_fig_p032_1.png]
Figure 1.6
Figure 1.6. Figure 1.6: Different challenges in crowd counting; (a) occluded scene, (b) low resolution image, (c) non-uniform distribution, (d) complex background images [PITH_FULL_IMAGE:figures/full_fig_p033_1_6.png]
Figure 1
Figure 1. Figure 1 [PITH_FULL_IMAGE:figures/full_fig_p034_1.png]
Figure 1.8
Figure 1.8. Figure 1.8: Annotated vs. un-annotated crowd image [PITH_FULL_IMAGE:figures/full_fig_p038_1_8.png]
Figure 1.9
Figure 1.9. Figure 1.9: Significance of Crowd Scene Analysis Significance of Crowd Scene Analysis Venue Management Public Transportation Management Retail Analytics Political Event Management Disaster Management Smart City Planning Security and Survelliance [PITH_FULL_IMAGE:figures/full_fi…
Figure 2.1
Figure 2.1. Figure 2.1: Different Approaches for Crowd Counting 2.1.1 Traditional Approaches: A. Detection Based Approaches: Earlier approaches to crowd counting relied primarily on detection-based methods. Detection models are used to create annotations on the persons/objects present in an…
Figure 3
Figure 3. Figure 3: (a) [PITH_FULL_IMAGE:figures/full_fig_p072_3.png]
Figure 3.1
Figure 3.1. Figure 3.1: (a) Training and testing error due to over-fitting, (b) Training and testing error with [PITH_FULL_IMAGE:figures/full_fig_p073_3_1.png]
Figure 3
Figure 3. Figure 3 [PITH_FULL_IMAGE:figures/full_fig_p073_3.png]
Figure 3.2
Figure 3.2. Figure 3.2: Different Approaches for Data Augmentation [PITH_FULL_IMAGE:figures/full_fig_p074_3_2.png]
Figure 3
Figure 3. Figure 3 [PITH_FULL_IMAGE:figures/full_fig_p074_3.png]
Figure 3.3
Figure 3.3. Figure 3.3: Traditional vs. Deep Learning based Approaches for Data Augmentation [PITH_FULL_IMAGE:figures/full_fig_p075_3_3.png]
Figure 3
Figure 3. Figure 3 [PITH_FULL_IMAGE:figures/full_fig_p081_3.png]
Figure 3.4
Figure 3.4. Figure 3.4: Architecture of a Simple Neural Network Artificial neural networks (as shown in [PITH_FULL_IMAGE:figures/full_fig_p082_3_4.png]
Figure 3.5
Figure 3.5. Figure 3.5: General Architecture of Artificial Neural Network [PITH_FULL_IMAGE:figures/full_fig_p082_3_5.png]
Figure 3.6
Figure 3.6. Figure 3.6: Artificial Neural Network and its resemblance with human brain [PITH_FULL_IMAGE:figures/full_fig_p083_3_6.png]
Figure 3.7
Figure 3.7. Figure 3.7: Mathematical Structure of Artificial Neural Network with 5-inputs and 1-output [PITH_FULL_IMAGE:figures/full_fig_p084_3_7.png]
Figure 3
Figure 3. Figure 3 [PITH_FULL_IMAGE:figures/full_fig_p084_3.png]
Figure 3.8
Figure 3.8. Figure 3.8: Backpropagated Neural Networks – How it works? [PITH_FULL_IMAGE:figures/full_fig_p085_3_8.png]
Figure 3
Figure 3. Figure 3 [PITH_FULL_IMAGE:figures/full_fig_p085_3.png]
Figure 3.9
Figure 3.9. Figure 3.9: General Architecture of Convolutional Neural Network [PITH_FULL_IMAGE:figures/full_fig_p086_3_9.png]
Figure 3
Figure 3. Figure 3 [PITH_FULL_IMAGE:figures/full_fig_p087_3.png]
Figure 3.10
Figure 3.10. Figure 3.10: General Architecture of Multi Column Convolutional Neural Network (MCNN) [PITH_FULL_IMAGE:figures/full_fig_p088_3_10.png]
Figure 3
Figure 3. Figure 3: 11: Density Map 3.2 End-to-end Proposed Framework: The proposed framework Self-supervised multi-column convolutional neural network for crowd counting aims to count the number of people in a crowded scene. The proposed model consists of two stages; Stage one is dedicat…
Figure 3.12
Figure 3.12. Figure 3.12: Proposed architecture for crowd counting using self-supervised training. Stage 1 [PITH_FULL_IMAGE:figures/full_fig_p091_3_12.png]
Figure 3
Figure 3. Figure 3 [PITH_FULL_IMAGE:figures/full_fig_p092_3.png]
Figure 3.13
Figure 3.13. Figure 3.13: Proposed Model for Crowd Counting [PITH_FULL_IMAGE:figures/full_fig_p092_3_13.png]
Figure 3
Figure 3. Figure 3 [PITH_FULL_IMAGE:figures/full_fig_p093_3.png]
Figure 3.14
Figure 3.14. Figure 3.14: Single branch of M-CNN: Sequence of Convolution and Pooling operations [PITH_FULL_IMAGE:figures/full_fig_p093_3_14.png]
Figure 3.15
Figure 3.15. Figure 3.15: Proposed Architecture of M-CNN [PITH_FULL_IMAGE:figures/full_fig_p094_3_15.png]
Figure 3
Figure 3. Figure 3 [PITH_FULL_IMAGE:figures/full_fig_p095_3.png]
Figure 3.16
Figure 3.16. Figure 3.16: Proposed Architecture of Stage 1 for Crowd Counting. Self-Supervised training [PITH_FULL_IMAGE:figures/full_fig_p096_3_16.png]
Figure 3
Figure 3. Figure 3 [PITH_FULL_IMAGE:figures/full_fig_p098_3.png]
Figure 3.17
Figure 3.17. Figure 3.17: Proposed Architecture of Stage 2 for Crowd Counting. SINKHORN Matching [PITH_FULL_IMAGE:figures/full_fig_p099_3_17.png]
Figure 4
Figure 4. Figure 4 [PITH_FULL_IMAGE:figures/full_fig_p105_4.png]
Figure 4
Figure 4. Figure 4: 2 Results on Images from ShanghaiTech PartA with occluded scenes [PITH_FULL_IMAGE:figures/full_fig_p106_4.png]
Figure 4.3
Figure 4.3. Figure 4.3: Results on Images from ShanghaiTech PartA with complex background [PITH_FULL_IMAGE:figures/full_fig_p107_4_3.png]
Figure 4.4
Figure 4.4. Figure 4.4: Overall Rotational Accuracy for classification of rotation angle on ShanghaiTech Part A [PITH_FULL_IMAGE:figures/full_fig_p108_4_4.png]
Figure 4.5
Figure 4.5. Figure 4.5: Overall training loss during self-supervised training [PITH_FULL_IMAGE:figures/full_fig_p108_4_5.png]
Figure 4.6
Figure 4.6. Figure 4.6: Results on Images from ShanghaiTech PartB with scale in-variation [PITH_FULL_IMAGE:figures/full_fig_p109_4_6.png]
Figure 4.7
Figure 4.7. Figure 4.7: Results on Images from ShanghaiTech PartB with Complex Background [PITH_FULL_IMAGE:figures/full_fig_p109_4_7.png]
Figure 4.8
Figure 4.8. Figure 4.8: Overall Rotational Accuracy for classification of rotation angle on ShanghaiTech Part B [PITH_FULL_IMAGE:figures/full_fig_p110_4_8.png]
Figure 4
Figure 4. Figure 4 [PITH_FULL_IMAGE:figures/full_fig_p110_4.png]
Figure 5
Figure 5. Figure 5 [PITH_FULL_IMAGE:figures/full_fig_p112_5.png]
Figure 5.1
Figure 5.1. Figure 5.1: ) comprises of three different blocks as described below [PITH_FULL_IMAGE:figures/full_fig_p112_5_1.png]
Figure 5
Figure 5. Figure 5: ) [PITH_FULL_IMAGE:figures/full_fig_p114_5.png]
Figure 5
Figure 5. Figure 5 [PITH_FULL_IMAGE:figures/full_fig_p115_5.png]
Figure 5
Figure 5. Figure 5 [PITH_FULL_IMAGE:figures/full_fig_p117_5.png]
Figure 5
Figure 5. Figure 5 [PITH_FULL_IMAGE:figures/full_fig_p119_5.png]
Figure 5
Figure 5. Figure 5 [PITH_FULL_IMAGE:figures/full_fig_p120_5.png]
Figure 5
Figure 5. Figure 5 [PITH_FULL_IMAGE:figures/full_fig_p121_5.png]
Figure 5
Figure 5. Figure 5 [PITH_FULL_IMAGE:figures/full_fig_p122_5.png]
Figure 6.1
Figure 6.1. Figure 6.1: Violent Video: Frames 1 to 20 [PITH_FULL_IMAGE:figures/full_fig_p129_6_1.png]
Figure 6.2
Figure 6.2. Figure 6.2: Confusion Matrix for Experimental Results on Hockey Fight Dataset [PITH_FULL_IMAGE:figures/full_fig_p130_6_2.png]
Figure 6.3
Figure 6.3. Figure 6.3: Model Accuracy on Hockey Fight Dataset [PITH_FULL_IMAGE:figures/full_fig_p131_6_3.png]
Figure 6.4
Figure 6.4. Figure 6.4: Model Loss on Hockey Fight Dataset [PITH_FULL_IMAGE:figures/full_fig_p131_6_4.png]
Figure 6.5
Figure 6.5. Figure 6.5: Confusion Matrix for Experimental Results on SCVD 6.5.2 Evaluation Results [PITH_FULL_IMAGE:figures/full_fig_p132_6_5.png]
Figure 6.6
Figure 6.6. Figure 6.6: Model Accuracy on SCVD [PITH_FULL_IMAGE:figures/full_fig_p132_6_6.png]
Figure 6.7
Figure 6.7. Figure 6.7: Model Loss on SCVD [PITH_FULL_IMAGE:figures/full_fig_p133_6_7.png]

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Forward citations

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Reference graph

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Pith tools

Reviewed August 15, 2026 · model on record in the stance chip above.