Abstract: At present, there are still some problems in the application of UAV video target tracking algorithms. For example, the tracking effect is not good when the illumination is uneven, the target rotates, and the target is blocked. Therefore, this paper proposes a CamShift tracking algorithm combining HLBP feature matching with Kalman filtering. First, the target features are extracted by the HLBP algorithm to obtain more accurate texture features, and then the interference caused by the change in illumination and the target rotation to the feature extraction is reduced. Second, the degree of occlusion of the target is judged by Bhattacharyya distance. Finally, the Kalman filter algorithm is used in the prediction of the target position, which can effectively solve the problem of poor tracking effect when the target is blocked. The experimental results show that in the practical application of UAV target tracking, the improved algorithm can effectively reduce the impact of external interference on the tracking effect, and the tracking accuracy is improved.

Key words: UAV, target tracking, CamShift tracking algorithm, HLBP texture processing, Kalman filtering algorithm