Anomalous event detection and localization in dense crowd scenes

Alhothali, Areej; Balabid, Amal; Alharthi, Reem; Alzahrani, Bander A.; Alotaibi, Reem; Barnawi, Ahmed

doi:10.1007/s11042-022-13967-w

Cited by 10 publications

(3 citation statements)

References 84 publications

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“…The approach was designed to be efficient and have a low computational cost, making it suitable for real-time applications. In [22], abnormal behaviours were detected in a two-step process. Initially, the Yolov5 model was employed for detection, while the DeepSORT model was utilised for tracking.…”

Section: Related Workmentioning

confidence: 99%

“…These results demonstrate the variety of techniques applied to different datasets and the corresponding performance metrics, providing insights into the effectiveness of various approaches in crowd anomaly detection. [28] UCF-Crime 70.4% AUC CNN [29] ShanghaiTech 240.0 MAE, 260.5 MSE CNN, Random Forest [23] HajjV2 76.08% AUC Optical Flow [30] ShanghaiTech 89.29% AUC CNN, Histogram of Optical Flow, SVM [22] HajjV2 88.96% AUC gKLT + Collectiveness Energy Index (CEI) [31] UMN Scene 1: 92.32%, Scene 3: 94.2%…”

Section: Related Workmentioning

confidence: 99%

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Efficient Crowd Anomaly Detection Using Sparse Feature Tracking and Neural Network

Altowairqi,

Luo,

Greer

et al. 2024

Applied Sciences

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Crowd anomaly detection is crucial in enhancing surveillance and crowd management. This paper proposes an efficient approach that combines spatial and temporal visual descriptors, sparse feature tracking, and neural networks for efficient crowd anomaly detection. The proposed approach utilises diverse local feature extraction methods, including SIFT, FAST, and AKAZE, with a sparse feature tracking technique to ensure accurate and consistent tracking. Delaunay triangulation is employed to represent the spatial distribution of features in an efficient way. Visual descriptors are categorised into individual behaviour descriptors and interactive descriptors to capture the temporal and spatial characteristics of crowd dynamics and behaviour, respectively. Neural networks are then utilised to classify these descriptors and pinpoint anomalies, making use of their strong learning capabilities. A significant component of our study is the assessment of how dimensionality reduction methods, particularly autoencoders and PCA, affect the feature set’s performance. This assessment aims to balance computational efficiency and detection accuracy. Tests conducted on benchmark crowd datasets highlight the effectiveness of our method in identifying anomalies. Our approach offers a nuanced understanding of crowd movement and patterns by emphasising both individual and collective characteristics. The visual and local descriptors facilitate high-level analysis by closely relating to semantic information and crowd behaviour. The analysis observed shows that this approach offers an efficient framework for crowd anomaly detection, contributing to improved crowd management and public safety. The proposed model achieves accuracy of 99.5 %, 96.1%, 99.0% and 88.5% in the UMN scenes 1, 2, and 3 and violence in crowds datasets, respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Efficient Crowd Anomaly Detection Using Sparse Feature Tracking and Neural Network

Altowairqi,

Luo,

Greer

et al. 2024

Applied Sciences

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show abstract

“…Alhothali et al. [9] proposed a two‐step YOLO‐V5 deep learning‐based approach for detecting abnormal behavior in surveillance videos, and an SVM (Support Vector Machine) classifier is used for evaluating the model. This system is slow and needs improvement in the real‐time domain.…”

Section: Related Workmentioning

confidence: 99%

Real‐time object‐removal tampering localization in surveillance videos by employing YOLO‐V8

Sandhya,

Kashyap

2024

Journal of Forensic Sciences

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Videos are considered as most trustworthy means of communication in the present digital era. The advancement in multimedia technology has made video content sharing and manipulation very easy. Hence, the video authenticity is a challenging task for the research community. Video forensics refer to uncovering the forgery traces. The detection of spatiotemporal object‐removal forgery in surveillance videos is crucial for judicial forensics, as the presence of objects in the video has significant information as legal evidence. The author proposes a passive max–median averaging motion residual algorithm for revealing the forgery traces, successfully giving visible object‐removal traces followed by a deep learning approach, YOLO‐V8, for forged region localization. YOLO‐V8 is the latest deep learning model, which has a wide scope for real‐time application. The proposed method utilizes YOLO‐V8 for object‐removal forgery in surveillance videos. The network is trained on the SYSU‐OBJFORG dataset for object‐removal forged region localization in videos. The fine‐tuned YOLO‐V8 successfully classifies and localizes the object‐removal tampered region with an F1‐score of 0.99 and a precision of 0.99. The observed high confidence score of the bounding box around the forged region makes the model reliable. This fine‐tuned YOLO‐V8 would be a better choice in real‐time applications as it solves the complex object‐based forgery detection in videos. The performance of the proposed system is far better than the existing deep learning approach.

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Anomaly Behavior Detection in Crowd via Lightweight 3D Convolution

Wang,

Xie

2024

Lecture Notes in Computer Science

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Anomalous event detection and localization in dense crowd scenes

Cited by 10 publications

References 84 publications

Efficient Crowd Anomaly Detection Using Sparse Feature Tracking and Neural Network

Efficient Crowd Anomaly Detection Using Sparse Feature Tracking and Neural Network

Real‐time object‐removal tampering localization in surveillance videos by employing YOLO‐V8

Anomaly Behavior Detection in Crowd via Lightweight 3D Convolution

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