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30 October 2009 Online real AdaBoost with co-training for object tracking
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Proceedings Volume 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis; 749503 (2009)
Event: Sixth International Symposium on Multispectral Image Processing and Pattern Recognition, 2009, Yichang, China
One of the major challenges of object tracking is to tackle appearance variations, possibly caused by the change of object postures, size, and occlusions. In this paper an adaptive tracking system is presented, which integrates online semisupervised classification and particle filter efficiently. To identify object pixels from background accurately, classifiers are trained online using real Adaboost which performs much better than its discrete version. In the system, uncorrelated features, color and texture are adopt to train two classifiers separately; the classifiers fused by voting generate confidence score for each pixel measuring its belonging to object or background in candidate regions; accumulated scores in each region are feed to particle filter for estimating object states; pixels with high scores augment the training set mutually and further classifiers are updated by co-training. The system is applied to vehicle and pedestrian tracking in real world scenarios and the experimental results show its robustness to large appearance variations and severe occlusions.
© (2009) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Lizuo Jin, Zhiguo Bian, Xiaobing Li, Hong Pan, and Siyu Xia "Online real AdaBoost with co-training for object tracking", Proc. SPIE 7495, MIPPR 2009: Automatic Target Recognition and Image Analysis, 749503 (30 October 2009);

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