针对传统跟踪算法在复杂场景下抗遮挡能力和鲁棒性差的问题,提出一种基于深度特征自适应融合的运动目标跟踪算法。考虑到深层特征强鲁棒性和浅层特征高精度的优点,首先利用稀疏自编码器构建深度稀疏特征以提取目标特征,再根据相邻帧之间的关联信息和跟踪置信度对深度特征和纹理信息进行自适应融合以提高跟踪器的性能。为了提高跟踪算法鲁棒性的同时抑制跟踪漂移,当置信度低于设定阈值时,引入改进SURF算法对目标进行定位。实验结果表明:与主流跟踪算法相比,所提算法的跟踪精度高,在遮挡场景中具有良好的鲁棒性,并且能够有效抑制跟踪漂移。

In this paper, we propose a moving target tracking algorithm based on the adaptive fusion of depth futures. This algorithm is aimed at solving the problems of poor anti-occlusion ability and robustness of traditional tracking algorithms in complex scenes. Considering the strong robustness of deep features and the advantages of high precision of shallow features, the deep sparse features are constructed using the sparse autoencoder to extract target features. Then, the depth features are adjusted according to the correlation information between adjacent frames as well as tracking confidence adaptive fusion with texture information to improve the tracker performance. To improve the robustness of the tracking algorithm while suppressing tracking drift when the confidence is lower than the set threshold, we introduce an improved speeded up robust features algorithm to locate the target. Experimental results show that the proposed algorithm has higher tracking accuracy, better robustness in occlusion scenes, and can effectively suppress tracking drift compared with the mainstream tracking algorithms.