联合Transformer与BYTE数据关联的多目标实时跟踪算法

Multitarget Real-Time Tracking Algorithm Based on Transformer and BYTE Data

针对复杂环境下多目标跟踪过程出现的轨迹漏检、误检及身份切换等问题,提出一种基于改进YOLOX和BYTE数据关联方法的多目标跟踪算法.首先,为了增强YOLOX在复杂环境下的目标检测能力,将YOLOX骨干网络与Vision Transformer结合,增强网络的局部特征提取能力,同时加入α-GIoU损失函数,进一步增加网络边界框的回归精度;其次,为了满足算法实时性要求,采用BYTE数据关联方法,摒弃传统特征重识别(Re-ID)网络,进一步提高了多目标跟踪算法的速度;最后,为了改善光照、遮挡等复杂环境下的跟踪问题,采用更加适应非线性系统的扩展卡尔曼滤波,提高了网络在复杂场景下对跟踪轨迹的预测精度.实验结果表明:所提算法对MOT17数据集的multiple object tracking accuracy(MOTA)、identity F1-measure(IDF1)指标分别为73.0%、70.2%,相较于目前最优的ByteTrack,分别提升了 1.3个百分点、2.1个百分点,number of identity switches(IDSW)则减少了 3.7%;同时所提算法取得了 51.2 frame/s的跟踪速度,满足系统实时性要求.

To solve the problems of trajectory missed detection,misdetection,and identity switching in complex multitarget tracking,this paper proposes a multitarget tracking algorithm based on improved YOLOX and BYTE data association methods.First,to enhance YOLOX’s target detection capabilities in complex environments,we combine the YOLOX backbone network and Vision Transformer to improve the network’s local feature extraction capability and add theα-GIoU loss function to further improve the regression accuracy of the network bounding box.Second,to meet the realtime requirements of the algorithm,we employ the BYTE data association method,abandon the traditional feature rerecognition(ReID)network,and further improving the speed of the proposed multitarget tracking algorithm.Finally,to mitigate the tracking problems in complex environments,such as illumination and occlusion,we adopt the extended Kalman filter,which is more adaptive to the nonlinear system,to improve the prediction accuracy of the network for tracking trajectory in complex scenes.The experimental results show that the multiple object tracking accuracy(MOTA)and identity F1-measure(IDF1)of the proposed algorithm on the MOT17 dataset are 73.0%and 70.2%,respectively,compared with the current optimal algorithm ByteTrack,they are improved by 1.3 percentage points and 2.1 percentage points,respectively,whereas number of identity switches(IDSW)is reduced by 3.7%.Meanwhile,the proposed algorithm achieves a tracking speed of 51.2 frames/s,which meets the realtime requirements of the system.