基于梯度方向直方图特征的多核跟踪  

Multiple Kernels Based Object Tracking Using Histograms of Oriented Gradients

作　　者：贾慧星[1,2] 章毓晋[1,2] 

机构地区：[1]清华大学信息科学与技术国家实验室,北京100084 [2]清华大学电子工程系,北京100084

出　　处：《自动化学报》2009年第10期1283-1289,共7页Acta Automatica Sinica

基　　金：国家自然科学基金(60872084)资助~~

摘　　要：提出了基于梯度方向直方图特征的多核跟踪算法,对跟踪过程中的光线变化和部分遮挡具有较强的鲁棒性.该算法将目标分块,分别提取出每块的核函数加权的梯度方向直方图特征.目标模型和候选目标模型的相似度用所有块直方图间的Bhattacharyya系数之和进行度量,目标的跟踪通过Mean shift算法最大化两者的相似度实现.对车辆、人体等多个日标的跟踪验证了本文提出算法的有效性.A novel multiple kernels based object tracking algorithm using histograms of oriented gradients is proposed in this paper, which is robust to illumination change and partial occlusion. The algorithm divides the object into blocks and extracts kernel weighted histograms of oriented gradients for each block. The similarity between target model and candidate model is measured by the sum of Bhattacharyya coefficients of all the corresponding histograms. The object is tracked by maximizing the similarity measure using the mean shift algorithm. Experiments on the tracking of vehicle and human demonstrate the effectiveness of the proposed algorithm.

关 键 词：Mean SHIFT 核跟踪 BHATTACHARYYA系数 梯度方向直方图 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]