CAJ | 학술논문

针对变化场景下的目标鲁棒跟踪,提出了一种结合均值漂移与Kalman滤波的跟踪算法.利用YCbCr特征空间进行目标描述,使用Kalman滤波对目标运动速度和空间位置进行预测.根据干扰的不同情况,使用不同的比例因子将两算法的跟踪结果线性加权得到目标的最终位置,并利用一种比较科学的模型更新策略,减轻了模型漂移的影响,视频序列跟踪结果表明,提出的方法能够稳定地进行跟踪.
침대변화장경하적목표로봉근종,제출료일충결합균치표이여Kalman려파적근종산법.이용YCbCr특정공간진행목표묘술,사용Kalman려파대목표운동속도화공간위치진행예측.근거간우적불동정황,사용불동적비례인자장량산법적근종결과선성가권득도목표적최종위치,병이용일충비교과학적모형경신책략,감경료모형표이적영향,시빈서렬근종결과표명,제출적방법능구은정지진행근종.
To deal with the robustness of object tracking in the time-variant scene,an algorithm combined Kalman filter with Mean-Shift algorithm is proposed in this paper. We employ YCbCr features to describe the object, and Kalman filter is used to predict the position and velocity of the target. According to different disturbance circumstances, the two algorithms tracking results are done with liner weight method by using different scale factors to get the final position of the target. Furthermore, a model update strategy is utilized to alleviate the model drift. Experimental results show the good performances of the proposed algorithm.