Open AccessDetecting abnormal events in traffic video surveillance using superorientation optical flow feature
Author(s) -
Athanesious Joshan,
Srinivasan Vasuhi,
Vijayakumar Vaidehi,
Christobel Shiny,
Sethuraman Sibi Chakkaravarthy
Publication year - 2020
Publication title -
iet image processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.401
H-Index - 45
eISSN - 1751-9667
pISSN - 1751-9659
DOI - 10.1049/iet-ipr.2019.0549
Subject(s) - optical flow , computer science , artificial intelligence , cluster analysis , computer vision , motion (physics) , pattern recognition (psychology) , feature (linguistics) , frame (networking) , motion detection , image (mathematics) , philosophy , linguistics , telecommunications
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.