Object Tracking by a Combination of Discriminative Global and Generative Multi-Scale Local Models
AbstractObject tracking is a challenging task in many computer vision applications due to occlusion, scale variation and background clutter, etc. In this paper, we propose a tracking algorithm by combining discriminative global and generative multi-scale local models. In the global model, we teach a classifier with sparse discriminative features to separate the target object from the background based on holistic templates. In the multi-scale local model, the object is represented by multi-scale local sparse representation histograms, which exploit the complementary partial and spatial information of an object across different scales. Finally, a collaborative similarity score of one candidate target is input into a Bayesian inference framework to estimate the target state sequentially during tracking. Experimental results on the various challenging video sequences show that the proposed method performs favorably compared to several state-of-the-art trackers. View Full-Text
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Song, Z.; Sun, J.; Yu, J. Object Tracking by a Combination of Discriminative Global and Generative Multi-Scale Local Models. Information 2017, 8, 43.
Song Z, Sun J, Yu J. Object Tracking by a Combination of Discriminative Global and Generative Multi-Scale Local Models. Information. 2017; 8(2):43.Chicago/Turabian Style
Song, Zhiguo; Sun, Jifeng; Yu, Jialin. 2017. "Object Tracking by a Combination of Discriminative Global and Generative Multi-Scale Local Models." Information 8, no. 2: 43.
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