JDE multi-object tracking algorithm integrating multi-level semantic enhancement

融合多阶语义增强的JDE多目标跟踪算法

¹ School of Electronics and Information Engineering, Xi′an Technological University, Xi′an 710021, China
² Development Planning Service, Xi′an Technological University, Xi′an 710021, China
³ Shaanxi Academy of Aerospace Technology Application Co., Ltd, Xi′an 710100, China

Received: 3 November 2021

Abstract

In order to solve the problem of target ID switching caused by target occlusion and insufficient ID information and location information extraction in JDE(joint detection and embedding) algorithm, an improved multi-target tracking algorithm based on JDE is proposed in this paper. Firstly, the SPA feature space pyramid attention module is used to expand the receptive field and obtain more abundant semantic information to improve the detection accuracy of the model for different scale targets. Secondly, the FCN network makes the header and ID Embedding task collaborative learning to alleviate the excessive competition and enhance the original semantic information, effectively reducing the number of ID switching. Finally, PCCs-Ma motion measurement can strengthen the connection between Kalman filtering prediction and observation, and improve the reliability of similarity discrimination of motion characteristics. In order to verify the effectiveness of the algorithm, the JDE algorithm and the proposed algorithm are compared in the same experimental environment. The experimental results show that the average accuracy of model detection is improved by 3.94 %. On the MOT16 dataset, the MOTA and IDF1 indexes are increased by 6.9 %, and the number of ID switching of the improved algorithm is significantly reduced, and good tracking results are achieved.

摘要

为了解决联合检测和嵌入(JDE)算法中目标遮挡以及ID信息与位置信息提取不足造成的目标ID切换问题，提出了融合多阶语义增强的JDE多目标跟踪方法。采用SPA特征空间金字塔注意力模块扩大感受野，获得更丰富的语义信息, 提高模型对不同尺度目标的检测精度；通过FCN网络使检测头和ID Embedding任务协同学习以缓解两者的过度竞争并增强原始语义信息，有效减少ID切换次数；利用PCCs-Ma运动度量加强卡尔曼滤波的预测和观察之间的联系，提高运动特征相似度判别的可靠性。为了验证算法的有效性，设计了相同实验环境下JDE算法和所提算法的对比实验。实验结果表明，所提算法模型检测平均精度提高了3.94%。在MOT16数据集上，MOTA和IDF1指标均提高了6.9%，改进后的算法ID切换次数明显减少，取得了良好的跟踪效果。