<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>In our paper, we have proposed to use graphs to detect anomaly in human action video. Although the detection of anomaly is a widely researched topic, but very few researchers have detected anomaly in action video using graphs. in our proposed method we have represented the smaller section (sub-section) of our input video as a graph where vertices of the graph are the space time interest points in the sub-section video and the association between the space time interest points exists. Thus, graphs for each sub section are created to look for a repeated substructure. We believe most of the actions inherently are repeated in nature. Thus, we have tried to capture the repetitive sub-structure of the action represented as a graph and used this repetitive sub-structure to compress the graph. If the compressed graph has few elements that have not been compressed, we suspect them as anomaly. But the threshold value takes care not to make the proposed method very much sensitive towards the few uncompressed elements. Our proposed method has been implemented on locally created “extended KTH” and “extended Weizmann” datasets with good accuracy score. The proposed method can also be extended for few more applications such as training athletes and taking elderly care.</p>
</abstract>

In our paper, we have proposed to use graphs to detect anomaly in human action video. Although the detection of anomaly is a widely researched topic, but very few researchers have detected anomaly in action video using graphs. in our proposed method we have represented the smaller section (sub-section) of our input video as a graph where vertices of the graph are the space time interest points in the sub-section video and the association between the space time interest points exists. Thus, graphs for each sub section are created to look for a repeated substructure. We believe most of the actions inherently are repeated in nature. Thus, we have tried to capture the repetitive sub-structure of the action represented as a graph and used this repetitive sub-structure to compress the graph. If the compressed graph has few elements that have not been compressed, we suspect them as anomaly. But the threshold value takes care not to make the proposed method very much sensitive towards the few uncompressed elements. Our proposed method has been implemented on locally created “extended KTH” and “extended Weizmann” datasets with good accuracy score. The proposed method can also be extended for few more applications such as training athletes and taking elderly care.

Graph based anomaly detection in human action video sequence

Department of Computer Science and Business Systems

Bharati Vidyapeeth Deemed to be University College of Engineering

Department of Electronics and Communication Engineering

Indira Gandhi Delhi Technical University for Women

Journal of Electrical Engineering

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Graph based anomaly detection in human action video sequence"}

In our paper, we have proposed to use graphs to detect anomaly in human action video. Although the detection of anomaly is a widely researched topic, but very...

Graph based anomaly detection in human action video sequence

Published Online: Nov 15, 2022

Page range: 318 - 324

Received: Aug 15, 2022

DOI: https://doi.org/10.2478/jee-2022-0042

Keywords
anomaly detection, graphs interpretation, substructures, space-time interest points

© 2022 Pranoti Shrikant Kavimandan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Graph based anomaly detection in human action video sequence

Published Online: Nov 15, 2022

Page range: 318 - 324

Received: Aug 15, 2022

DOI: https://doi.org/10.2478/jee-2022-0042

Keywordsanomaly detection, graphs interpretation, substructures, space-time interest points

© 2022 Pranoti Shrikant Kavimandan et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Keywords
anomaly detection, graphs interpretation, substructures, space-time interest points