基于注意力机制和卡尔曼滤波的多目标跟踪

为了解决目前多目标跟踪算法在行人遮挡后无法再次准确跟踪的问题,提出了一种融入注意力机制和改进卡尔曼滤波的多目标跟踪算法,选用联合检测和重识别框架提取特征,同时完成目标检测和重识别任务.设计了并行支路注意力机制,包括空间注意力和通道注意力两个部分,每个部分都采用并行支路的方式完成池化和卷积操作.在跟踪阶段,本文提出了速度先验卡尔曼滤波,实现对行人运动状态更精确的预测.采用CUHK-SYSU数据集对算法进行训练,并在MOT16数据集上做算法的验证和测试.本算法的多目标跟踪准确度(MOTA)达到了65.1％,多目标跟踪精确度(MOTP)达到了78.8％,识别F1值(IDF1)达到62.3％.实验表明,提出的跟踪算法可以有效地提高跟踪的整体性能,实现对目标的持续跟踪.

Multi-Target Tracking Using Attention Mechanism and Kalman Filter

Given that the existing multi-target tracking algorithm cannot track accurately after occlusion, a multi-target tracking algorithm using the improved attention mechanism and Kalman filter is proposed. The structure of joint detection and embedding is used to extract features and accomplish object detection and identification simultaneously. A parallel-structured attention mechanism is proposed, containing both spatial and channel parts. Each part is designed into parallel branches for pooling and convolution. During tracking, the proposed velocity-prediction Kalman filter is adopted for the more accurate prediction of pedestrian movements. The CUHK-SYSU dataset is used for training, and the algorithm is verified and tested on the MOT16 dataset. The proposed algorithm can achieve 65.1% MOTA, 78.8% MOTP, and 62.3% IDF1. The experimental results show that the proposed tracking algorithm can improve the overall tracking performance and achieve continuous tracking.