Abnormal activity detection using shear transformed spatio-temporal regions at the surveillance network edge
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Abnormal activity detection using shear transformed spatio-temporal regions at the surveillance network edge Michael George1
· Babita Roslind Jose1 · Jimson Mathew2
Received: 12 September 2019 / Revised: 10 May 2020 / Accepted: 26 June 2020 / © Springer Science+Business Media, LLC, part of Springer Nature 2020
Abstract This paper presents a method of detecting abnormal activity in crowd videos while considering the direction of the dominant crowd motion. One main goal of our approach is to be able to run at the edge of the surveillance network close to the surveillance cameras so as to reduce network congestion and decision latency. To capture motion features while considering the direction of dominant crowd direction we propose a generalised shear transform based spatio-temporal region. To detect abnormal activity, an autoencoder based method is adopted considering the requirement for running the method at the network edge. During training, the autoencoder learns motion features for each spatio-temporal region from video frames containing normal activity. While testing, those motion features from each spatio-temporal region that cannot be reconstructed satisfactorily by the autoencoder indicate abnormal activity. This approach allows coarse localisation as well as detection of abnormal activity. The approach demonstrated O(n) behaviour with ability to work at higher frame rates by trading off accuracy. The approach has been verified against recent works on standard abnormal activity datasets: UCSD dataset and Subway dataset. Keywords Events · Actions and activity recognition · Internet of things · Video surveillance architectures · Image/video surveillance and analytics
Michael George
[email protected] Babita Roslind Jose [email protected] Jimson Mathew [email protected] 1
School of Engineering, Cochin University of Science and Technology, Kochi, Kerala 682022, India
2
Department of Computer Science and Engineering, Indian Institute of Technology Patna, Patna, Bihar 801103, India
Multimedia Tools and Applications
1 Introduction The recent years have seen a boom in the number of surveillance cameras being installed for purposes like security [45], crowd control [57], patient care [36] etc. This has also consequently resulted in an explosion in the amount of video surveillance data that needs to be analysed, particularly in a smart city environment [46]. Two paradigms could be adopted for such a large scale video surveillance network as seen in Fig. 1. In cloud computing the video is processed at a centralised location. While in edge computing [10, 40] the video is processed using models at the edge of the surveillance network. Cisco [11] predicts that by 2021 half the workload will need to be run outside datacenters. They suggest an edge computing based approach as a solution. Cisco also predicts that between 2017 and 2022, there will be a seven fold increase [12] in video surveillance data that will be introduced into the internet as traffic. To quantify the amount of data that would ne
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