基于重要性加权的结构稀疏跟踪方法

针对视觉跟踪中描述目标能力的有限性和局部稀疏表示模型的有效性,提出了一种基于重要性加权的结构稀疏跟踪方法.该方法采用结构稀疏表示对目标表观建模,根据在表达目标表观时所起的作用,对每个局部图像进行加权处理;在粒子滤波框架下,应用最大后验概率对目标的状态进行估计;通过带有遮挡检测机制的模板更新策略对目标模板进行在线的更新以避免跟踪漂移.实验表明,该方法有效地减弱了目标表观变化对模型的影响,对于视频序列中的遮挡、光照变化和目标姿态改变等有稳健的跟踪效果.

Structural Sparse Tracking Method Based on Importance Weighting

According to the limitations of describe target capabilities and the effectiveness of local sparse representa-tion model in visual tracking,a structural sparse tracking method based on importance weighting is proposed aimed to the deficiencies. In the method,we adopt sparse representation to model object and according to important degree of expressing object,each local image is weighted to improve robustness of target model. In the framework of particle filtering,based on maximum a posteriori probability to estimate target. In addition,a template updating strategy of occlusion detection mechanism is used to real-time update template to avoid tracking drift. Experimental results show that the proposed method can effectively reduce the influence of the object apparent change on the model and our method is competitive to the state-of-the-art trackers on challenging video sequences with illumination changes, background clutter,occlusion,fast motion and deformation.