基于特征在线选择的目标压缩跟踪算法

Object Compressive Tracking via Online Feature Selection

基于压缩感知理论的压缩跟踪算法能够有效地实现对目标的跟踪,具有良好的实时性,但该算法对目标特征没有进行在线选择导致跟踪鲁棒性不高.本文提出一种基于特征在线选择的目标压缩跟踪算法.首先,在目标附近采样得到正负样本集合,计算样本的多尺度矩形特征,采用压缩感知中的随机投影矩阵对高维特征投影得到低维压缩域特征,对压缩域特征进行在线选择提取最优特征,剔除被污染的样本特征,使用简单高效的朴素贝叶斯分类模型进行样本判断,实现对目标的跟踪,同时对跟踪中目标在摄像头中的尺度变化进行建模,给出目标尺度变化的定量描述,实现了适应目标尺度变化的多尺度跟踪.实验结果表明本文算法具有更好的鲁棒性与更高的跟踪精度,对目标跟踪中的遮挡、光线突变、尺度变化和非刚性形变等因素具有较好的抗干扰能力,同时算法复杂度低,可以满足实时性要求.

The compressive tracking algorithm based on compressive sensing theory can efficiently achieve real-time object tracking, but the algorithm does not select proper object features online, resulting in low tracking robustness. In order to solve this problem, an object compressive tracking algorithm with online feature selection is presented. Firstly, sets of positive and negative samples are obtained by sampling around the object, and the multi-scale rectangle features of the samples are calculated. Secondly, the compressive sensing random projection matrix is used to reduce the dimensionality of high dimensional features to obtain low-dimensional compressive domain features, and the compressive domain features are updated and selected online to extract the optimal feature to remove contaminated samples and update the classifier.Finally, a simple and efficient Bayesian classification model is utilized to achieve the object tracking. Moreover, changes of object scale in the camera are modeled and a quantitative description of changes in scale is given for multi-scale tracking which can adapt to change of the object scale. Experimental results show that the proposed algorithm can achieve a higher tracking accuracy and better robustness than several state-of-the-art algorithms and can well respond to the interferences such as block in the object tracking, light mutation, scale changes, non-rigid deformation and so on. Meanwhile, it has a low computational complexity and fully satisfies the real-time requirement.