摘要： 针对在跟踪过程中目标易受到遮挡、姿态变化、光照变化、复杂背景等问题的影响,提出一种改进的鲁棒跟踪算法。采用局部块稀疏编码方式,结合尺度不变特征及空间金字塔特征匹配技术进行目标表观建模。在粒子滤波的框架下引入运动估计以获取目标最优位置,并在跟踪过程中实时更新模板。实验结果表明,与DFT、FCT、OAB等常用跟踪算法相比,该算法在复杂情况下仍能保持较好的跟踪鲁棒性。

关键词: 目标跟踪, 稀疏编码, 空间金字塔匹配, 粒子滤波, 运动估计

Abstract: Aiming at the influence of occlusion,pose change,illumination change,complex background and other issues during the tracking process,an improved robust tracking algorithm is proposed.The sparse coding method of local blocks is used to combine the SIFT features and spatial pyramid feature matching techniques to study the objective modeling method.In the framework of particle filtering,motion estimation is introduced to obtain the optimal target position,and the template is updated in real time during the tracking process.Experimental results show that compared with common tracking algorithms such as DFT,FCT and OAB,the algorithm can still maintain good tracking robustness under complex conditions.

Key words: object tracking, sparse coding, spatial pyramid matching, particle filtering, motion estimation

中图分类号: 

• TP391