中文摘要：

在光照、背景变化、遮挡、噪声、快速运动等复杂环境下,准确地实现行人跟踪一直是富有挑战性的任务。针对这些问题,提出基于HSV颜色特征和贡献度重构的行人跟踪算法。在粒子滤波的框架内,从HSV空间提取目标的混合颜色特征生成目标模板集,依据不同区域对跟踪结果的影响对区域进行贡献度分配,并将其引入到一个自适应的正则化模型中,将具有最小重构误差的区域判定为待跟踪目标。为了增强算法的稳健性,跟踪过程中对模板进行实时更新。在OTB 100个序列上进行测试,本文算法得到跟踪结果的平均中心误差和跟踪成功率两项指标分别为0.6624pixel和0.4153,优于同类其他算法。实验结果表明,该算法能够在复杂的视频场景中实现对行人的连续跟踪,且稳健性较好,有利于在实际系统中的实现。

英文摘要：

It is a challenging task to track pedestrian accurately in complicated environment such as illumination,background variation,occlusion,noise and fast motion.Aiming at these problems,the tracing algorithm based on HSV color features and reconstruction by contributions is proposed.The proposed algorithm extracts the mixed color features of target in HSV space to generate the target template set within the particle filter framework.According to the influence of different regions on the tracking results,the contribution of the region is distributed.And it is introduced into the adaptive regularization model,and the region with the minimum reconstruction error is determined as the target to be tracked.In order to be more robust,the templates are updated in real time during the tracking progress.The average center error of tracking results and tracking success rate of 100 sequences tested in OTB are 0.6624 pixel and 0.4153,respectively,and the proposed algorithm has better performance than others.Experimental results show that the proposed algorithm can realize the continuous tracking for pedestrian in complex video scenes and is beneficial to be realized in the practice system with better robustness.