基于Kalman和Surf的Camshift目标跟踪研究  

Camshift Target Tracking Based on Kalman and Surf

作　　者：王超[1] 苏湛[1] 

机构地区：[1]上海理工大学光电信息与计算机工程学院,上海200093

出　　处：《软件导刊》2018年第1期77-80,共4页Software Guide

基　　金：上海市自然科学基金项目(14ZR1428800;15ZR1428600)

摘　　要：传统的Camshift运动目标跟踪算法在目标遮挡或背景颜色干扰下,容易陷入局部最大值,造成目标跟踪丢失。针对这一问题,提出了一种结合Kalman滤波及Surf特征提取的改进算法。该算法需在视频序列中手动框选跟踪目标作为目标模板。将传统Camshift算法得到的目标候选区域与目标模板进行直方图对比,得到的巴氏系数若大于设定的阈值则说明目标跟踪丢失。采用Surf算法,在该帧图像中匹配出新的目标候选区域,最终得到候选区域的位置信息更新Kalman滤波。仿真实验表明,改进后的算法在复杂背景下仍然具有良好的跟踪效果。The traditional Camshift moving target tracking algorithm is easy to fall into the local maximum value and lost tracking target under the target occlusion or background color interference. An improved algorithm combining Kalman filter and Surf feature extraction is proposed. Firstly, it is necessary to manually select the target as a template region in the sequence of video frames while using this algorithm. Then calculate the Bhattacharyya coefficient of the candidate region obtained by the traditional Camshift algorithm and the template region by histogram matching. If the coefficient is greater than the threshold, this situa- tion represents lost tracking traget. The Surf algorithm is used to match the new target candidate region in the frame image. The Kalman filter is updated according to the position information of the finally obtained candidate region, The simulation results show that the improved algorithm still has a good tracking effect in the complex background.

关 键 词：CAMSHIFT算法 Kalman预测器 SURF算法 颜色干扰 目标遮挡 

分 类 号：TP312[自动化与计算机技术—计算机软件与理论]