Document Type
Conference Proceeding
Publication Date
2006
Abstract
We present a simple, principled approach to detecting foreground objects in video sequences in real-time. Our method is based on an on-line discriminative learning technique that is able to cope with illumination changes due to discontinuous switching, or illumination drifts caused by slower processes such as varying time of the day. Starting from a discriminative learning principle, we derive a training algorithm that, for each pixel, computes a weighted linear combination of selected past observations with time-decay. We present experimental results that show the proposed approach outperforms existing methods on both synthetic sequences and real video data.
Repository Citation
Cheng, L.,
Wang, S.,
& Caelli, T.
(2006). An Online Discriminative Approach to Background Subtraction. Proceedings of the IEEE International Conference on Video and Signal Based Surveillance.
https://corescholar.libraries.wright.edu/knoesis/280
DOI
10.1109/AVSS.2006.22
Included in
Bioinformatics Commons, Communication Technology and New Media Commons, Databases and Information Systems Commons, OS and Networks Commons, Science and Technology Studies Commons
Comments
This paper was presented at the IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), 2006, in Sydney, Australia.