基于CamShift和Kalman组合的改进目标跟踪算法

针对应用CamShift算法进行目标跟踪过程中,当目标被严重遮挡、目标被与目标颜色相近的背景干扰时易丢失跟踪目标的问题,提出了一种基于CamShift和Kalman滤波组合的改进跟踪算法；为克服目标因严重遮挡而丢失的缺陷,利用自适应算法改进了传统的CamShift算法,扩大了搜索窗口,使运动目标位于搜索窗口内；为解决目标因颜色相近背景干扰而丢失的问题,改善跟踪准确率,利用卡尔曼滤波预测目标运动空间位置,作为下一帧搜索窗口的质心坐标；基于上述改进,利用C++语言,研发了改进的CamShift目标跟踪软件模块,给出了该模块的算法流程；实验结果表明,改进后的目标跟踪算法能有效地克服传统CamShift算法的缺陷,大大提高运动目标跟踪的准确性；所提的算法可以应用于运动小车跟踪,人脸识别等领域。

Improved CamShift Algorithm Combined with Kalman Filter for Moving Target Tracking

A new target tracking algorithm is proposed that combines the modified CamShift algorithm with Kalman filter, aiming at several problems occurred in target tracking, such as the moving target being covered, and the target being interfered by similar background. In order to overcome the shortages of target loss due to the moving target being covered, the search window of the traditional CamShift algorithm is improved and the size of searching window is adjusted adaptively, so that the moving target is located in the search window. When the moving target is interfered by similar background, the target is lost. In order to increase the tracking accuracy, Kalman filter is used for estimating the position of the moving target which is used as the center location of search window in the next frame. An improved CamShift target tracking software module is developed using C ++ language, and the algorithm flow of the module is given. The experimental results show that the proposed algorithm can overcome the default of the traditional CamShift algorithm and improve the performance and accuracy of target tracking and location. This algorithm can be applied to the field of moving car tracking, face recognition etc.