Background estimation under rapid gain change in thermal imagery
Computer Vision and Image Understanding
IEEE Transactions on Pattern Analysis and Machine Intelligence
Track and cut: simultaneous tracking and segmentation of multiple objects with graph cuts
Journal on Image and Video Processing - Video Tracking in Complex Scenes for Surveillance Applications
Enhancing change detection in low-quality surveillance footage using markov random fields
VNBA '08 Proceedings of the 1st ACM workshop on Vision networks for behavior analysis
Hybrid tracking algorithms for planar and non-planar structures subject to illumination changes
ISMAR '06 Proceedings of the 5th IEEE and ACM International Symposium on Mixed and Augmented Reality
AVSS '09 Proceedings of the 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance
Joint tracking and segmentation of objects using graph cuts
ACIVS'07 Proceedings of the 9th international conference on Advanced concepts for intelligent vision systems
Segmentation of motion objects from surveillance video sequences using partial correlation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Incremental unsupervised three-dimensional vehicle model learning from video
IEEE Transactions on Intelligent Transportation Systems
Two-frame stereo photography in low-light settings: a preliminary study
Proceedings of the 9th European Conference on Visual Media Production
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Illumination changes are a ubiquitous problem in computer vision. They present a challenge in many applications, including tracking: for example, an object may move in and out of a shadow. We present a new tracking algorithm which is insensitive to illumination changes, while at the same time using all of the available photometric information. The algorithm is based on computing an illumination-invariant optical flow field; the computation is made robust by using a graph cuts formulation. Experimentally, the new technique is shown to quite reliable in both synthetic and real sequences, dealing with a variety of illumination changes that cause problems for density based trackers.