摘要
针对目前Camshift跟踪算法计算量大,实时性差的缺点,提出一种基于Kalman与Camshift相结合的方法,能够有效提高算法的实时性。通过目标分割算法得到目标物体的轮廓区域,计算出目标的质心,以目标质心坐标和运动速度作为Kalman滤波器输入预测出目标在下一帧中的位置,然后在预测位置附近用Camshift算法搜索和匹配,得到目标的精确位置信息,以此时得到的精确位置信息为Kalman滤波器的测量输入,参与到下一轮预测,依次循环进行下去。经过验证该方法在一定条件下有很好的准确性和实时性。
Current track Camshift algorithm has computationally intensive and poor real-time shortcoming,and a Kalman and Camshift based on a combination of methods is proposed,which can effectively improve algorithm real-time performance. Contour area of target object is gotten by object segmentation algorithm,and target centroid is estimated. Target coordinates centroid and speed is taken as Kalman filter input to predicte target position in the next frame. In searching and matching,rough and coarse position in the vicinity is adopted by using Camshift to give precise target location information,which obtaines accurate position information for Kalman filter measurement input and participates in the next round prediction,and follows by cycle to proceed. Simulation results show that the proposed method has good accuracy and timeliness under certain conditions.
出处
《沈阳理工大学学报》
CAS
2016年第6期77-81,87,共6页
Journal of Shenyang Ligong University
