NDLI: Autoencoder-based abnormal activity detection using parallelepiped spatio-temporal region

Content Provider	IET Digital Library
Author	George, Michael Jose, Babita Roslind Mathew, Jimson Kokare, Pranjali
Abstract	The spread of surveillance cameras has necessitated the monitoring of large quantities of surveillance video feeds. A manual monitoring system is near impossible due to the large man-hour requirements. Recently, automatic abnormal activity detection has been an area of interest among researchers. A spatio-temporal feature, histogram of optical flow orientation and magnitude (HOFM), has produced impressive ability in detecting abnormal activities. The authors propose a novel non-uniform spatio-temporal region resembling parallelepipeds, from which they extract the HOFM features. Autoencoders can be configured to detect abnormal patterns. The authors have used these abilities of the autoencoders to detect abnormalities in the HOFM features extracted from their novel spatio-temporal regions of the video feeds. The autoencoders are trained on the HOFM features of the videos containing no abnormalities. The autoencoders are then fed with the HOFM features of the videos to be tested for abnormal activities, and these are detected based on the abilities of the autoencoders to reconstruct these features. The proposed method is tested on the standard abnormality detection datasets: UCSD Ped1, UCSD Ped2, Subway Entrance, Subway Exit, and UMN.
Starting Page	23
Ending Page	30
Page Count	8
ISSN	17519632
Volume Number	13
e-ISSN	17519640
Issue Number	Issue 1, Feb (2019)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-cvi/13/1
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-cvi.2018.5240
Journal	IET Computer Vision
Publisher Date	2018-09-06
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Autoencoder-based Abnormal Activity Detection Automatic Abnormal Activity Detection Computer Vision And Image Processing Technique Feature Extraction Histogram of Optical Flow Orientation And Magnitude HOFM Feature Extraction Image Motion Analysis Image Recognition Image Reconstruction Image Sequence Manual Monitoring System Neural Computing Technique Neural Nets Novel Nonuniform Spatio-temporal Region Object Detection Parallelepiped Spatio-temporal Region Spatio-temporal Feature Standard Abnormality Detection Datasets Surveillance Cameras Surveillance Video Feeds Video Cameras Video Signal Processing Video Surveillance
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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