Occlusion-Aware Skeleton Trajectory Representation for Abnormal Behavior Detection

Temuroglu, Onur; Kawanishi, Yasutomo; Deguchi, Daisuke; Hirayama, Takatsugu; Ide, Ichiro; Murase, Hiroshi; Iwasaki, Mayuu; Tsukada, Atsushi

doi:10.1007/978-981-15-4818-5_9

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…• The definition of anomalous human behaviours can differ across applications. While most of the existing papers focused on detecting anomalous human behaviours in general, four papers focused on detecting anomalous behaviours for specific applications, that is, drunk walking [23], poor body movements in children [24], abnormal pedestrian behaviours at grade crossings [25] and crimebased anomalies [7]. Further, the nature of anomalous behaviours can vary depending upon various factors, like span of time, crowded scenes, and specific actionbased anomalies.…”

Section: Discussionmentioning

confidence: 99%

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%

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

Mishra¹,

Mihailidis²,

Khan³

2023

Preprint

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“…Gatt et al [22] used Long Short-Term Memory (LSTM) and 1-Dimensional Convolution (1DConv)-based AE models to detect abnormal human activities, including, but not limited to falls, using skeletons estimated from videos of a publicly available dataset. Temuroglu et al [23] proposed a skeleton trajectory representation that handled occlusions and an AE framework for pedestrian abnormal behaviour detection. The pedestrian video dataset used in this work was collected by the authors, where the training dataset was composed of normal walking, and the test dataset was composed of normal and drunk walking.…”

Section: A Reconstruction Approachesmentioning

confidence: 99%

“…The definition of anomalous human behaviours can differ across various applications. While most of the existing papers focused on detecting anomalous human behaviours in general, five papers focused on detecting anomalous behaviours for specific applications, including drunk walking [23], poor body movements in children [24], abnormal pedestrian behaviours at grade crossings [25], [26] and crime-based anomalies [7]. Moreover, the nature of anomalous behaviours can vary depending upon various factors, such as span of time, crowded scenes, and specific action-based anomalies.…”

Section: Fields Of Applicationmentioning

confidence: 99%

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

Mishra,

Mihailidis,

Khan

2024

IEEE Trans. Emerg. Top. Comput. Intell.

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The existing methods for video anomaly detection mostly utilize videos containing identifiable facial and appearancebased features. The use of videos with identifiable faces raises privacy concerns, especially when used in a hospital or communitybased 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 privacy-protecting alternative for video anomaly detection. Lastly, we identify major open research questions and provide guidelines to address them.

show abstract

Occluded Part-aware Graph Convolutional Networks for Skeleton-based Action Recognition

Kim,

Ju Kim,

Yoo

2024

2024 IEEE International Conference on Robotics and Automation (ICRA)

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Occlusion-Aware Skeleton Trajectory Representation for Abnormal Behavior Detection

Cited by 4 publications

References 14 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

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

Occluded Part-aware Graph Convolutional Networks for Skeleton-based Action Recognition

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