A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videos

Zeng, Xin; Jiang, Yalong; Ding, Wenrui; Li, Hongguang; Hao, Yafeng; Qiu, Zifeng

doi:10.1109/tcsvt.2021.3134410

Cited by 36 publications

(24 citation statements)

References 62 publications

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“…Further, the nature of anomalous behaviours can vary depending upon various factors, like span of time, crowded scenes, and specific actionbased anomalies. Some papers identified and addressed the need to detect specific types of anomalies, namely, multi-timescale anomalies occurring over different time duration [27], anomalies in both sparse and crowded scenes [28], fine and coarse-grained anomalies [36] and body movement and object position anomalies [40].…”

Section: Discussionmentioning

confidence: 99%

“…Sparse scene anomalies can be described as anomalies in scenes with less number of humans, while dense scene anomalies can be described as anomalies in crowded scenes with large number of humans [28]. It is comparatively difficult to identify anomalous behaviours in dense scenes than sparse scenes due to tracking multiple people and finding their individual anomaly scores [17].…”

Section: Discussionmentioning

confidence: 99%

“…A fully connected layer was used at the end of the network to predict future skeletons. Zeng et al [28] proposed a hierarchical spatio-temporal GCN, where highlevel representations encoded the trajectories of people and the interactions among multiple identities while low-level skeleton graph representations encoded the local body posture of each person. The method was proposed to detect anomalous human behaviours in both sparse and dense scenes.…”

Section: B Prediction Approachesmentioning

confidence: 99%

See 2 more Smart Citations

Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directions

Mishra¹,

Mihailidis²,

Khan³

2023

Preprint

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The existing methods for video anomaly detection mostly utilize videos containing identifiable facial and appearance-based features. The use of videos with identifiable faces raises privacy concerns, especially when used in a hospital or community-based setting. Appearance-based features can also be sensitive to pixel-based noise, straining the anomaly detection methods to model the changes in the background and making it difficult to focus on the actions of humans in the foreground. Structural information in the form of skeletons describing the human motion in the videos is privacy-protecting and can overcome some of the problems posed by appearance-based features. In this paper, we present a survey of privacy-protecting deep learning anomaly detection methods using skeletons extracted from videos. We present a novel taxonomy of algorithms based on the various learning approaches. We conclude that skeleton-based approaches for anomaly detection can be a plausible privacyprotecting alternative for video anomaly detection. Lastly, we identify major open research questions and provide guidelines to address them.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: B Prediction Approachesmentioning

confidence: 99%

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Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directions

Mishra¹,

Mihailidis²,

Khan³

2023

Preprint

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“…The aim of such algorithms is to recognize crimes and atypical behaviors and movements of pedestrians (including tripping and collapsing) based on patterns in a live transmission of the video material. The typical setup proposed in the literature [2,12,13,31,32] shares the same structure: First, the system determines the spatial location of limbs for all human bodies within the given scene. This is completed either by locating the pedestrians first and then determining the position of the limbs (top-down) or by determining the position of the limbs first (bottom-up), and then associating them to corresponding persons.…”

Section: Smart Video Surveillance Using Digital Skeletonsmentioning

confidence: 99%

“…The achieved output of the generally represented procedure is an anomaly feature. The exact kind of feature depends on the chosen algorithm and can range from heatmaps [2] to classified bounding boxes [12,13,31,32]. st 19, 2022 submitted to Appl.…”

mentioning

confidence: 99%

Perception of Risks and Usefulness of Smart Video Surveillance Systems

2022

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The number of video cameras in public places increases due to different reasons such as detecting dangers (e.g., thefts, robberies, terrorist attacks) and security breaches in crowds. The application of video surveillance systems is sometimes evaluated ambivalently; therefore, the presented study focuses on factors influencing the acceptance of a privacy-friendly, smart video surveillance system. Overall, 216 persons aged between 18 and 81 years participated in an online survey. In terms of the perceived usefulness, there are significant interactions of public spaces × gender and public spaces × time of day. In addition, the assessment of different privacy levels of a video surveillance system differ significantly in terms of perceived risk. Interestingly, men rate the risk concerning their own privacy significantly higher than women do. Participants rate the presented system as fairly useful and slightly risky for their own privacy. The findings of the presented exploratory study provide insight into how people perceive smart video surveillance. These findings have the potential to support the conditions of the use of smart video surveillance systems and to address the possibly affected individuals. smart video surveillance; intelligent video surveillance; machine learning; human pose estimation; privacy protection; online survey; exploratory study; time of day; public areas; gender; perceived privacy risk

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Innovative Video Anomaly Detection: TCN‐AnoDetect With Self‐Supervised Feature Learning

Rahul Chiranjeevi,

Malathi

2024

Trans Emerging Tel Tech

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Video anomaly detection is a critical task in surveillance, industrial quality control, and anomaly monitoring systems. Recognizing anomalous events or behaviors within video sequences is challenging due to the diverse and often vague nature of anomalies. A novel temporal convolutional network‐based anomaly detection (TCN‐AnoDetect) is proposed that leverages TCNs and self‐supervised learning. In this, TCNs are employed to model the temporal context within video sequences effectively, capturing short and long‐term dependencies. The algorithm integrates TCNs with pretrained models to encode rich spatiotemporal features. The core of TCN‐AnoDetect lies in self‐supervised feature learning, where a neural network is pretrained on unlabeled video data to capture high‐level spatiotemporal features. The anomaly detection module combines reconstruction‐based and temporal context–aware approaches, using reconstruction errors and temporal context deviations for anomaly scoring and classification. To enhance model robustness, TCN‐AnoDetect incorporates domain adaptation technique to handle domain shifts and evolving anomalies. The proposed algorithm is evaluated on three different benchmark datasets and ShanghaiTech Campus, demonstrating its superior performance. The extensive experiments performed in terms of different evaluation measures show the efficiency of the TCN‐AnoDetect algorithm. The TCN‐AnoDetect, an innovative approach, thereby provides promising solutions in video anomaly detection and in various applications.

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A Hierarchical Spatio-Temporal Graph Convolutional Neural Network for Anomaly Detection in Videos

Cited by 36 publications

References 62 publications

Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directions

Skeletal Video Anomaly Detection using Deep Learning: Survey, Challenges and Future Directions

Perception of Risks and Usefulness of Smart Video Surveillance Systems

Innovative Video Anomaly Detection: TCN‐AnoDetect With Self‐Supervised Feature Learning

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