NDLI: Detecting abnormal events in traffic video surveillance using superorientation optical flow feature

Content Provider	IET Digital Library
Author	Athanesious, Joshan Srinivasan, Vasuhi Vijayakumar, Vaidehi Christobel, Shiny Sethuraman, Sibi Chakkaravarthy
Abstract	Detection of abnormal events in the traffic scene is very challenging and is a significant problem in video surveillance. The authors proposed a novel scheme called super orientation optical flow (SOOF)-based clustering for identifying the abnormal activities. The key idea behind the proposed SOOF features is to efficiently reproduce the motion information of a moving vehicle with respect to superorientation motion descriptor within the sequence of the frame. Here, the authors adopt the mean absolute temporal difference to identify the anomalies by motion block (MB) selection and localisation. SOOF features obtained from MB are used as motion descriptor for both normal and abnormal events. Simple and efficient K-means clustering is used to study the normal motion flow during the training. The abnormal events are identified using the nearest-neighbour searching technique in the testing phase. The experimental outcome shows that the proposed work is effectively detecting anomalies and found to give results better than the state-of-the-art techniques.
Starting Page	1881
Ending Page	1891
Page Count	11
ISSN	17519659
Volume Number	14
e-ISSN	17519667
Issue Number	Issue 9, Jul (2020)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/14/9
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2019.0549
Journal	IET Image Processing
Publisher Date	2020-03-10
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Abnormal Activities Abnormal Event Detection Anomaly Detection Combinatorial Mathematics Computer Vision And Image Processing Technique Data Handling Technique Feature Extraction Image Motion Analysis Image Recognition Image Sequence K-means Clustering Algorithm Knowledge Engineering Technique Motion Block Localisation Motion Block Selection Nearest Neighbour Method Nearest-neighbour Searching Normal Motion Flow Object Detection Optimisation Technique Pattern Clustering Search Problem SOOF Feature Statistics Superorientation Motion Descriptor Superorientation Optical Flow Feature Traffic Engineering Computing Traffic Scene Traffic Video Surveillance Video Signal Processing Video Surveillance
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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