CAJ | 학술논문

CamShift算法应用于复杂背景及遮挡条件下视频跟踪时,极易出现跟踪失效和目标丢失.本文提出基于颜色、纹理及目标运动信息的综合特征用于改进CamShift算法,结合Kalman滤波器对目标运动状态进行预测提高了复杂背景下运动目标的跟踪稳定性和跟踪精度.在目标发生遮挡时,通过目标遮挡前的先验信息进行最小二乘拟合及目标运动轨迹外推,预测目标运动位置信息,有利于遮挡结束时对运动目标的重新捕获.多组实验结果及性能分析表明,该算法在复杂背景及目标被短时遮挡情况下,可以实现目标的持续、稳定跟踪,并具有较好的实时性.
CamShift산법응용우복잡배경급차당조건하시빈근종시,겁역출현근종실효화목표주실.본문제출기우안색、문리급목표운동신식적종합특정용우개진CamShift산법,결합Kalman려파기대목표운동상태진행예측제고료복잡배경하운동목표적근종은정성화근종정도.재목표발생차당시,통과목표차당전적선험신식진행최소이승의합급목표운동궤적외추,예측목표운동위치신식,유리우차당결속시대운동목표적중신포획.다조실험결과급성능분석표명,해산법재복잡배경급목표피단시차당정황하,가이실현목표적지속、은정근종,병구유교호적실시성.
Traditional CamShift algorithm has been widely applied in the field of video tracking, but it tends to fail in the complex background and occlusion condition. In this paper, we choose color, texture, and the target motion information as features based on traditional CamShift algorithm, and constantly predict the target state of motion combined with the Kalman filter, in order to improve tracking accuracy in the complex background. In the event of occlusion, we use least squares fitting and extrapolation to predict the target location through the priori motion information of the target before occlusion, and re-capture the target after occlusion. The experimental results show that the algorithm can still track the target well in the case of complex background and short-term occlusion and have good real time ability