论文标题
Atttrack：在线深层注意转移用于多对象跟踪
AttTrack: Online Deep Attention Transfer for Multi-object Tracking
论文作者
论文摘要
多对象跟踪（MOT）是智能视频分析应用程序（例如监视和自动驾驶）的重要组成部分。执行深度学习模型以进行视觉对象跟踪所需的时间和存储复杂性阻碍了它们在计算能力有限的嵌入式设备上的采用。在本文中，我们的目标是通过在培训和推理时间将知识从复杂网络（教师）的高级特征转移到轻量级网络（学生）来加速MOT。拟议的AttTrack框架具有三个关键组成部分：1）跨模型功能学习以使教师和学生模型中的中间表示相结合，2）在推理时交织了两个模型的执行，以及3）将来自教师模型的更新预测与先验知识相结合，以帮助学生模型。使用两个不同的对象检测骨架yolov5和DLA34在MOT17和MOT15数据集上进行了行人跟踪任务的实验，并且DLA34表明AttTrack可以显着改善学生模型跟踪性能，同时仅牺牲较小的跟踪速度的较小降级。
Multi-object tracking (MOT) is a vital component of intelligent video analytics applications such as surveillance and autonomous driving. The time and storage complexity required to execute deep learning models for visual object tracking hinder their adoption on embedded devices with limited computing power. In this paper, we aim to accelerate MOT by transferring the knowledge from high-level features of a complex network (teacher) to a lightweight network (student) at both training and inference times. The proposed AttTrack framework has three key components: 1) cross-model feature learning to align intermediate representations from the teacher and student models, 2) interleaving the execution of the two models at inference time, and 3) incorporating the updated predictions from the teacher model as prior knowledge to assist the student model. Experiments on pedestrian tracking tasks are conducted on the MOT17 and MOT15 datasets using two different object detection backbones YOLOv5 and DLA34 show that AttTrack can significantly improve student model tracking performance while sacrificing only minor degradation of tracking speed.
