基于多外观模型的自适应加权目标跟踪算法*

偏最小二乘(PLS)跟踪算法忽略特征间及外观模型间的差异，容易受到光照、遮挡等因素的影响，降低目标的跟踪精度.针对上述问题，文中提出基于多外观模型的自适应加权目标跟踪算法(AWMA).首先使用PLS对目标区域逐步建立多个外观模型.然后根据各外观模型中特征的重要性及目标的显著度建立自适应权重的综合模型，融合多个外观模型完成目标与样本的误差分析.最后使用粒子滤波实现目标跟踪.实验表明，文中算法能更有效地过滤噪声数据，提高目标跟踪的鲁棒性和时间性能.

Adaptive Weighted Object Tracking Algorithm Based on Multi-appearance Models

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.