Occlusion Robust Vehicle Tracking based on SOM (Self-Organizing Map)
WACV-MOTION '05 Proceedings of the IEEE Workshop on Motion and Video Computing (WACV/MOTION'05) - Volume 2 - Volume 02
International Journal of Computer Vision
Keeping multiple objects in the field of view of a single PTZ camera
ACC'09 Proceedings of the 2009 conference on American Control Conference
A novel object tracking algorithm based on discrete wavelet transform and extended Kalman filter
LSMS'07 Proceedings of the Life system modeling and simulation 2007 international conference on Bio-Inspired computational intelligence and applications
Computer Vision and Image Understanding
Tracking by means of geodesic region models applied to multidimensional and complex medical images
Computer Vision and Image Understanding
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Tracking an object in a sequence of images can fail due to partial occlusion or clutter. Robustness to occlusion can be increased by tracking the object as a set of "parts" such that not all of these are occluded at the same time. However, successful implementation of this idea hinges upon finding a suitable set of parts. In this paper we propose a novel segmentation, specifically designed to improve robustness against occlusion in the context of tracking. The main result shows that tracking the parts resulting from this segmentation outperforms both tracking parts obtained through traditional segmentations, and tracking the entire target. Additional results include a statistical analysis of the correlation between features of a part and tracking error, and identifying a cost function that exhibits a high degree of correlation with the tracking error.