基于超像素和判别稀疏的运动目标跟踪算法

针对目标遮挡、非刚性变换、光照变换等因素干扰产生的漂移问题，提出基于超像素和判别稀疏的运动目标跟踪算法。算法首先利用SLIC方法对运动目标的观测区域进行超像素分割，然后通过K-Means算法构建包含目标和背景的超像素字典，再基于判别稀疏表示和?1]范数最小化框架求解候选目标的稀疏系数，同时结合粒子滤波框架和在线字典更新策略完成目标跟踪。实验结果表明，该算法在多种因素干扰的环境中具有较强的鲁棒性，能够准确稳定地进行在线目标跟踪。

Object tracking algorithm based on superpixel and discriminative sparsity

According to the drift problems caused by occlusion, deformation and illumination, an algorithm based on superpixel and discriminative sparsity is proposed. Firstly, the object observation area is segmented into superpixels by using SLIC method. Secondly, K-Means algorithm is adopted to generate superpixel dictionary combined with target and background information. Then, sparse coefficients of candidate targets are solved based on discriminative sparsity representation and ?1]-norm minimization framework. Finally, particle filter framework and online dictionary updating are applied to accomplish object tracking. Both quantitative and qualitative experimental results show the proposed algorithm performs more robustly under the environment of multiple interference factors, and it also can perform online object tracking more accurately and steadily.