DocumentCode :
1721480
Title :
Detecting Uncommon Trajectories
Author :
Wiliem, Arnold ; Madasu, Vamsi ; Boles, Wageeh ; Yarlagadda, Prasad
Author_Institution :
Sch. of Eng. Syst., Queensland Univ. of Technol., Brisbane, QLD
fYear :
2008
Firstpage :
398
Lastpage :
404
Abstract :
An effective video surveillance system relies on detection of suspicious activities. In recent times, there has been an increasing focus on detecting anomalies in human behaviour using surveillance cameras as they provide a clue to preventing breaches in security. Human behaviour can be termed as suspicious when it is uncommon in occurrences and deviates from commonly understood behaviour within a particular context. This work aims to detect regions of interest in video sequences based on an understanding of uncommon behaviour. A commonality value is calculated to distinguish between common and uncommon occurrences. The proposed strategy is validated by classifying walking path of the people in a shopping mall corridor. CAVIAR database is used for this purpose. The results demonstrate the efficacy of the proposed approach in detecting deviant walking paths.
Keywords :
image sequences; object detection; video surveillance; human behaviour anomaly detection; security breach prevention; uncommon trajectory detection; video sequence; video surveillance system; walking path detection; Cameras; Computer applications; Computer vision; Digital images; Humans; Legged locomotion; Monitoring; Security; Surveillance; Systems engineering and theory; computer vision; human behaviour; security; smart surveillance system; suspicious behaviour;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Digital Image Computing: Techniques and Applications (DICTA), 2008
Conference_Location :
Canberra, ACT
Print_ISBN :
978-0-7695-3456-5
Type :
conf
DOI :
10.1109/DICTA.2008.45
Filename :
4700049
Link To Document :
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