通道裁剪下的多特征组合目标跟踪算法

针对深层特征存在冗余通道影响跟踪速度和精度以及单一特征难以适应复杂场景的问题,提出了一种通道裁剪下的多特征组合目标跟踪算法。首先,在相关滤波算法的框架上结合传统手工特征和深层特征进行跟踪。其次,通过对比深层特征通道上目标区域和搜索区域的特征均值设计通道裁剪策略,筛选出合适的特征通道。最后,通过隔帧更新的方式更新深度特征,通过平均峰值相关能量更新传统特征滤波模板,最终实现准确跟踪。与10种算法在OTB2013和OTB2015数据集上进行对比实验的结果表明,本文算法在跟踪准确度和成功率方面都取得了更为理想的结果。

Multi-feature combined target tracking algorithm based on channel clipping

Aiming to solve the problems that the redundant channel in depth features affects the tracking speed and accuracy, and the single feature cannot adapt to all complex scenes, a multi-feature combined target tracking algorithm based on channel clipping is proposed. Firstly, the traditional manual features and depth features are combined for tracking. Secondly, a channel clipping strategy is designed by comparing the deep feature means of each channel in the target and searching areas. Then, the depth feature is updated by the proposed interval updating strategy, and the traditional feature filter template is updated by the average peak correlation energy. Compared with the 10 algorithms on the OTB2013 and OTB2015 datasets, the results show that the proposed algorithm achieves better results in tracking accuracy and success rate.