Online Real Boosting for Object Tracking Under Severe Appearance Changes and Occlusion
Li Xu, Takayoshi Yamashita, Shihong Lao, Masato Kawade, Feihu Qi · 2007
Robust visual tracking is always a challenging but yet intriguing problem owing to the appearance variability of target objects. In this paper we propose a novel method to handle large changes in appearance based on online real-value boosting, which is utilized to incrementally learn a strong classifier to distinguish between objects and their background. By incorporating online real boosting into a particle filter framework, our tracking algorithm shows a strong adaptability for different target objects which undergo severe appearance changes during the tracking process.