Discriminative Appearance Modeling with Multi-track Pooling for Real-time Multi-object Tracking
This work addresses a specific bottleneck in multi-object tracking for applications like surveillance or autonomous driving, but it is incremental as it builds on existing memory-based approaches.
The paper tackled the problem of similar-looking objects in multi-object tracking by proposing a multi-track pooling module and an online training strategy, achieving real-time, state-of-the-art performance on public datasets.
In multi-object tracking, the tracker maintains in its memory the appearance and motion information for each object in the scene. This memory is utilized for finding matches between tracks and detections and is updated based on the matching result. Many approaches model each target in isolation and lack the ability to use all the targets in the scene to jointly update the memory. This can be problematic when there are similar looking objects in the scene. In this paper, we solve the problem of simultaneously considering all tracks during memory updating, with only a small spatial overhead, via a novel multi-track pooling module. We additionally propose a training strategy adapted to multi-track pooling which generates hard tracking episodes online. We show that the combination of these innovations results in a strong discriminative appearance model, enabling the use of greedy data association to achieve online tracking performance. Our experiments demonstrate real-time, state-of-the-art performance on public multi-object tracking (MOT) datasets.