融合层次特征和混合注意力的目标跟踪算法

Object tracking algorithm with hierarchical features and hybrid attention

目标跟踪任务中,全卷积孪生网络的目标跟踪(SiamFC)算法在目标遮挡、光照变化等场景时会表现出鲁棒性较差、丢失跟踪目标等问题,为此提出一种结合特征融合和注意力机制的目标跟踪算法。首先,采用ResNet50作为主干网络提取更充分的目标特征;其次,结合注意力机制对特征进行筛选,将筛选后的低层模板特征与高层模板特征分别同对应搜索特征做互相关操作后进行自适应加权融合,提升网络对正负样本的辨别力。在OTB100数据集上测试,所提算法的精度和成功率分别为81.25%和64.06%;在LaSOT数据集上测试,该算法的精度和成功率分别为49.4%和50.1%。实验结果表明,该算法目标跟踪性能优于全卷积孪生网络算法,且在处理复杂场景时有更好的鲁棒性。

In object tracking tasks,Fully-Convolutional Siamese network for object tracking(SiamFC)algorithm has problems such as poor robustness and loss of tracking objects under the scenes of object occlusion and illumination variation.Therefore,an object tracking algorithm combining attention mechanism and feature fusion was proposed.Firstly,ResNet50(Deep Residual Network)was used as the backbone network to extract more adequate object features.Secondly,attention mechanism was used to filter features.After low-level template features and high-level template features were correlated with the corresponding search features,the adaptive weighted fusion was carried out to improve the discrimination of positive and negative samples.Tested on the OTB100(Object Tracking Benchmark)dataset,the proposed algorithm had the precision and success rate of 81.25% and 64.06%.Tested on the LaSOT(high-quality benchmark for Large-scale Single Object Tracking)dataset,the proposed algorithm had the precision and success rate of 49.4% and 50.1%.Experimental results show that the object tracking performance of the proposed algorithm is better than that of the fully convolutional Siamese network algorithm,and it has better robustness when dealing with complex scenes.