基于柔性特征优化的目标稳健跟踪

Flexible Feature Optimization Based Robust Object Tracking

针对单一特征空间不足以对动态时变环境中跟踪目标进行准确表达的缺点,提出一种基于柔性加权特征的Particle Filter目标跟踪算法.首先引入"陡峭因子"这一概念对不同特征的跟踪鉴别性能进行客观评估,然后参照当前不同特征的可跟踪性能以加权组合的方式自适应生成当前最优特征,最后将生成的最优特征嵌入到Particle Filter跟踪构架中完成目标跟踪任务.该算法具备较高的柔性可对任意采用直方图表达的特征进行自适应融合.不同的视频序列实验表明该算法可动态地对异类特征进行有效融合,对复杂场景下的目标进行稳健跟踪.

To overcome the disadvantages of single feature that often fails in describing the object reliably under dynamic environment, a flexible feature optimization based particle filter tracking algorithm is proposed. Firstly, the concept of sharpness factor is introduced to objectively elevate the discriminant ability for different features. Then, based on the feature＇s tracking property, the optimal feature under current scene is adaptively generated by combining the weighted features. Finally, the optimal feature is applied in the particle filter scheme to execute the object tracking task. The proposed algorithm is flexible and it can be extended to any feature represented by histogram. The experimental results on various videos demonstrate the effectiveness and robustness of the proposed method in multi-features fusion and object tracking.