结合SURF与Kalman滤波的CAMShift跟踪算法  

Camshift tracking algorithm of combined with SURF and Kalman fliter

作　　者：张磊[1] 彭力[1] 

机构地区：[1]江南大学物联网学院,无锡214122

出　　处：《电子测量与仪器学报》2017年第3期389-394,共6页Journal of Electronic Measurement and Instrumentation

基　　金：国家自然科学基金(61374047);江苏省产学研创新项目基金(BY2014023-36;BY2014023-25)资助项目

摘　　要：针对传统的CAMShift目标跟踪算法,在出现颜色干扰,遮挡等复杂背景中容易跟丢的问题,提出了一种结合SURF特征匹配与Kalman滤波的CAMShift跟踪算法。该算法利用CAMShift算法跟踪得到的候选目标与模板目标的色度和梯度方向的综合直方图比较计算得到的Bhattacharyya系数作为判定依据,当系数大于给定阈值时,采用SURF算法对搜索窗口和上一帧跟踪结果进行特征匹配,重新计算目标的大小和位置。同时为了避免目标快速运动时跟踪失败和减少SURF匹配的计算量,利用Kalman滤波对运动目标窗口进行预测更新以确定下一帧搜索窗口的中心位置。实验表明,该算法在图像背景复杂,出现颜色干扰以及部分遮挡时能够稳定跟踪,其跟踪速度与结合SURF的CAMShift算法相比有显著提高。In this paper,a tracking algorithm based on CAMShift which combined with SURF feature matching and Kalman filter is proposed to deal with the problems in traditional CAMShift algorithm,such as tracking failure under color interference or occlusion.The algorithm calculates the Bhattacharyya coefficient of integrated histogram composed of chroma feature and gradient direction feature between candidate target and template target as judging basis,it uses CAMShift algorithm.As the coefficient more than the threshold,SURF algorithm will be used to match the search window and the tracking result of the previous frame,then recalculate the target's size and position by the matching result.To avoid tracking failure by fast-moving of target and reduce the computation of SURF matching,the center position of moving target in the next frame will be predicted by Kalman predictor.The experimental results show that the new algorithm can achieve stable tracking object against complex backgrounds,color interference or occlusion,and have higher tracking speed than CAMShift algorithm combined with SURF.

关 键 词：目标跟踪 CAMSHIFT算法 KALMAN滤波 SURF算法 BHATTACHARYYA系数 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]