On-Line Object Behaviour Analysis for Surveillance Systems

article in a collection out of WoS and Scopus

English

Automatic surveillance systems are an important emerging application of on-line simple or compound event detection algorithms in video or audio data. The nature of such systems implies several requirements on the used algorithms. The system ability to give the response on-line is the main topic addressed in this work. The paper predefines the requirements for each system recognition module that must work in real-time or faster. This paper describes and analyses several event recognition algorithms based on video processing, concerning the applicability in on-line surveillance systems. Two main trends are addressed with focus on the methods' speed and robustness. Statistical approaches are the basis for simple event detectors whereas spatiotemporal rules define compound event detectors. Examples of statistical approaches are ad-hoc bicycle detector, dog detector based on AdaBoost classifier and exploitation of Hidden-Markov-Models for trajectory classification. The compound event approach is demonstrated on detection of "dangerous occurrence of the person on the platform edge" in underground scenario. The methods process the video data from standard low-resolution CCTV surveillance system. The developed approaches are evaluated on real data and applied in the real sites in underground scenarios.

video surveillance, object detection, on-line system, object behaviour analysis

BERAN, V.; JURÁNEK, R.; MLÍCH, J.; ŽÁK, P.; HEROUT, A.; ZEMČÍK, P.

2009

14. 9. 2009

Nairobi

1

5

5

https://www.fit.vut.cz/research/publication/9044/

Beran_Online_Approaches_for_Video_Survaillance.pdf