Abstract Partial least squares (PLS) tracking algorithm ignores the differences among features and those among appearance models. The corresponding tracking is easily affected by the factors, such as illumination and occlusion, and thereby the tracking accuracy decreases. To address these problems in application, an adaptive weight object tracking algorithm based on multi-appearance model (AWMA) is proposed. Firstly, the PLS method is used to gradually establish multiple appearance models for the target region. Then, according to the importance of features and significant degree of object in each appearance model, a comprehensive model with adaptive weights is built. Furthermore, the error analysis between object and sample is accomplished by integrating multiple appearance models. Finally, particle filter is used to achieve object tracking. The experimental results show that the proposed algorithm can effectively filter the noise data and improve tracking robustness and efficiency.
Fund:Supported by National Natural Science Foundation of China (No.61170223), Joint Funds of National Natural Science Foundation of China (No.U1204610), Young Scientists Fund of National Natural Science Foundation of China (No.61502434,61502432), Education Department Project of Henan Province (No.15A520099)
About author:: ZHU Zhenfeng, born in 1980, Ph.D., associate professor. His research interests include machine learning, pattern recognition and computer vision. YANG Haobo, born in 1991, master student. His research interests include object tracking. YE YangdongCorresponding author, born in 1962, Ph. D., professor. His research interests include intelligent systems, machine learning and database.
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2016, Vol. 29 
