基于卷积注意力模块和无锚框检测网络的行人跟踪算法

Pedestrian Tracking Algorithm Based on Convolutional Block Attention Module and Anchor-free Detection Network

针对多目标跟踪过程中遮挡严重时的目标身份切换、跟踪轨迹中断等问题,该文提出一种基于卷积注意力模块(CBAM)和无锚框(anchor-free)检测网络的行人跟踪算法。首先,在高分辨率特征提取网络HrnetV2的基础上,对stem阶段引入注意力机制,以提取更具表达力的特征,从而加强对重识别分支的训练;其次,为了提高算法的运算速度,使检测和重识别分支共享特征权重且并行运行,同时减少头网络的卷积通道数以降低参数运算量;最后,设定合适的参数对网络进行充分的训练,并使用多个测试集对算法进行测试。实验结果表明,该文算法相较于FairMOT在2DMOT15,MOT17,MOT20数据集上的精确度分别提升1.1%,1.1%,0.2%,速度分别提升0.82,0.88,0.41 fps;相较于其他几种主流算法拥有最少的目标身份切换次数。该文算法能够更好地适用于遮挡严重的场景,实时性也有所提高。

According to the target identity switch and tracking trajectory interruption,a multi-pedestrian tracking algorithm based on Convolutional Block Attention Module(CBAM)and anchor-free detection network is proposed.Firstly,attention mechanism is introduced to HrnetV2′s stem stage to extract more expressive features,thus strengthening the training of re-recognition branch.Secondly,in order to improve the operation speed of algorithm,detection task and recognition one share feature weights and are carried out simultaneously.Meanwhile,the convolutional channel’s number and parameter amount are reduced in the head network.Finally,the network is fully trained with proper parameters,and the algorithm is validated by multiple test sets.Experimental results show that compared with FairMOT,the accuracy of the proposed algorithm on2DMOT15,MOT17 and MOT20 data sets is improved by 1.1%,1.1%,0.2%respectively,and the speed is improved by 0.82,0.88 and 0.41 fps respectively.Compared with other mainstream algorithms,the proposed algorithm has the least number of target identity switching.The proposed algorithm improves effectively realtime performance of network model,which could be better applied to the scenes with severe occlusion.