Discriminative sparse representation and online dictionary learning for target tracking
Traditional sparse representation can not effectively distinguish between target and background.Aiming at these problems,a discriminative sparse representation was proposed,and a discriminative function to the traditional sparse was added for greatly reducing the influence of interference factors.While an online dictionary learning algorithm based on discrimination sparse representation and probabilistic mode was proposed to upgrade target template.It can effectively reduce the impact of the target and the background of the target template.The proposed tracker was empirically compared with state-of-the-art trackers on some challenging video sequences.Both quantitative and qualitative comparisons showed that our proposed tracker was superior and more stable.
sparse representation discriminative function dictionary learning probabilistic mode target template
HuangYue Peng Li
school of IoT technology Wuxi institute of technology Wuxi,China school of IoT Engineering Jiangnan University Wuxi,China
国际会议
贵阳
英文
324-327
2015-08-18（万方平台首次上网日期，不代表论文的发表时间）