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Graph stream mining based anomalous event analysis

conference contribution
posted on 2018-01-01, 00:00 authored by M Yang, L Rashidi, Sutharshan RajasegararSutharshan Rajasegarar, C Leckie
© Springer Nature Switzerland AG 2018. A major challenge in video surveillance is how to accurately detect anomalous behavioral patterns that may indicate public safety incidents. In this work, we address this challenge by proposing a novel architecture to translate the crowd status problem in videos into a graph stream analysis task. In particular, we integrate crowd density monitoring and graph stream mining to identify anomalous crowd behavior events. A real-time tracking algorithm is proposed for automatic identification of key regions in a scene, and at the same time, the pedestrian flow density between each pair of key regions is inferred over consecutive time intervals. These key regions are represented as the nodes of a graph, and the directional pedestrian density flow between regions is used as the edge weights in the graph. We then use Graph Edit Distance as the basis for a graph stream analysis approach, to detect time intervals of anomalous flow activity and to highlight the anomalous regions according to the heaviest subgraph. Based on the experimental evaluation on four real-world datasets and a benchmark dataset (UCSD), we observe that our proposed method achieves a high cross correlation coefficient (approximately 0.8) for all four real-world datasets, and 82% AUC with 28% EER for the UCSD datasets. Further, they all provide easily interpretable summaries of events using the heaviest subgraphs.

History

Event

Pacific Rim International Conference on Artifical Intelligence ( 15th : 2018 : Nanjing, China)

Volume

11012

Series

Lecture Notes in Computer Science

Pagination

891 - 903

Publisher

Springer

Location

Nanjing, China

Place of publication

Cham, Switzerland

Start date

2018-08-28

End date

2018-08-31

ISSN

0302-9743

eISSN

1611-3349

ISBN-13

9783319973036

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2018, Springer Nature Switzerland AG

Editor/Contributor(s)

Xin Geng, Byeong-Ho Kang

Title of proceedings

PRICAI 2018: Proceedings of the Pacific Rim International Conference on Artifical Intelligence: Trends in Artificial Intelligence