基于改进协作目标外观模型的在线视觉跟踪

Online Visual Tracking Based on Improved Collaborative Appearance Model

在不受限制的复杂环境中在线跟踪任意类型的感兴趣目标仍是一项极具挑战的难题.本文在无模型跟踪框架基础上提出一种基于改进协作目标外观模型的在线视觉跟踪方法,解决了大多数协作模型类跟踪算法在学习阶段无法有效选择正、负样本的问题.该方法根据人类视觉感知准则将目标边缘信息视为最具区分度的目标特征,提出边缘判别模型并结合动态模型和检测模块建立二级似然匹配空间,为生成模型的似然匹配去除了背景干扰;采用分块策略建立目标生成模型,为模型引入空间结构信息;利用Mean-Shift计算各子块的最终位置和匹配系数,并根据子块匹配系数为遮挡处理和模型更新提供依据.在公开视频序列上同几种流行视觉跟踪算法的对比实验结果证明了本文算法的有效性和优越性.

It is still a very challenging issue to online track arbitrary targets in the unrestricted complex environment.This paper presents an online visual tracking method with improved collaborative appearance model based on model-free framework,solving the problem of most other tracking algorithms with collaborative model,which is unable to effectively select the positive and negative samples.According to the human visual perception rules,object edge information is regarded as the most discriminative feature,on which an edge discriminative appearance model is proposed.In order to remove background interference in likelihood matching space for generative model,a two-stage matching space is put forward via integrating dynamic model,detection module and edge discriminative model.The generative model based on partition strategy is constructed for space and appearance information.The final position and matching coefficient of each sub-block are calculated by mean-shift,as a basis for occlusion handling and model update.Experimental results using challenging public video sequences show the effectiveness and superiority of the proposed method,compared with other state-of-the-art visual tracking approaches.