AttTrack: Online Deep Attention Transfer for Multi-object Tracking
This addresses the computational bottleneck for MOT in applications like surveillance and autonomous driving, but it is incremental as it builds on existing knowledge transfer methods.
The paper tackled the problem of accelerating multi-object tracking (MOT) for embedded devices by transferring knowledge from a complex teacher network to a lightweight student network, resulting in significantly improved tracking performance with only minor speed degradation on MOT17 and MOT15 datasets.
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.