基于改进CamShift融合Kalman滤波的无人机目标跟踪研究

Target Tracking for UAVs Based on Improved CamShift and Kalman Filter

针对无人机目标跟踪过程中CamShift算法对目标颜色相似背景干扰和遮挡干扰鲁棒性差问题,对CamShift算法进行了改进。首先,针对CamShift算法模板信息单一,易受到颜色相似背景干扰的问题,提出基于H分量和LBP二维直方图模板的CamShift目标跟踪算法,改进算法提高了算法对相似目标干扰的鲁棒性,且有效帧率提高了约21%;针对目标跟踪过程中目标易受到障碍物遮挡的问题,在CamShift算法中引进了Kalman滤波预测机制,增强了跟踪算法在目标遮挡条件下的鲁棒性和跟踪效率,其中跟踪效率提高了约25%,每帧迭代所用时间下降了约36%。

For target tracking carried out by Unmanned Aerial Vehicles （UAVs）, CamShift algorithm is easily influenced by color similar background interference and sheltering interference, thus we made improvement to the algorithm. Firstly, considering that the tracking template of CamShift has little information and is easily influenced by color similar background, we proposed an improved CamShift algorithm based on 2D histogram template with H and LBP, by which the robustness to color similar background was improved, and the valid frame rate was increased by about 21%. To the problem of easily influenced by sheltering interference in target tracking, the Kalman prediction algorithm was introduced into CamShift algorithm, which could improve the robustness and tracking efficiency under sheltering interference, with the valid frame rate increased by about 25% and the average iterative time of per frame decreased by 36%.