NDLI: Abnormal event detection and localisation in traffic videos based on group sparse topical coding

Content Provider	IET Digital Library
Author	Ahmadi, Parvin Tabandeh, Mahmoud Gholampour, Iman
Abstract	In visual surveillance, detecting and localising abnormal events are of great interest. In this study, an unsupervised method is proposed to automatically discover abnormal events occurring in traffic videos. For learning typical motion patterns occurring in such videos, a group sparse topical coding (GSTC) framework and an improved version of it are applied to optical flow features extracted from video clips. Then a very simple and efficient algorithm is proposed for GSTC. It is shown that discovered motion patterns can be employed directly in detecting abnormal events. A variety of abnormality metrics based on the resulting sparse codes for detection of abnormality are investigated. Experiments show that the result of the approach in detection and localisation of abnormal events is promising. In comparison with other usual methods (probabilistic latent semantic analysis, latent Dirichlet allocation, sparse topical coding (STC) and improved STC), according to the values of area under ROC, the proposed method achieves at least 14% improvement in abnormal event detection.
Starting Page	235
Ending Page	246
Page Count	12
ISSN	17519659
Volume Number	10
e-ISSN	17519667
Issue Number	Issue 3, Mar (2016)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/10/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2015.0399
Journal	IET Image Processing
Publisher Date	2016-03-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Abnormal Event Detection Abnormal Event Localisation Feature Extraction Group Sparse Topical Coding Image And Video Coding Latent Dirichlet Allocation Optical Flow Feature Extraction Probabilistic Latent Semantic Analysis Traffic Video Video Coding Video Signal Processing Visual Surveillance
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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