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• W1969077851 abstract "ABSTRACT One of the major challenges of object trac king 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 semi-supervised 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 tr ain two classifiers separately; the classifi ers fused by voting generate confidence score for each pixel measuring its belonging to object or background in candida te regions; accumulated scores in each region are feed to particle filter for estimating object states; pixels with high scores augmen t 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. Keywords: object tracking, online real Adaboo st, co-training, particle filter" @default.
• W1969077851 created "2016-06-24" @default.
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• W1969077851 date "2009-10-30" @default.
• W1969077851 modified "2023-09-23" @default.
• W1969077851 title "Online real AdaBoost with co-training for object tracking" @default.
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• W1969077851 doi "https://doi.org/10.1117/12.833166" @default.
• W1969077851 hasPublicationYear "2009" @default.
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