An Approximate Dynamic Programming Framework for Occlusion-Robust Multi-Object Tracking

TL;DR

This paper introduces ADPTrack, a dynamic programming-based framework that enhances multi-object tracking accuracy by reducing occlusion errors, especially effective in fixed-camera scenarios.

Contribution

The paper presents ADPTrack, a novel approximate dynamic programming approach that improves existing multi-object tracking methods by considering multiple frames to handle occlusions more effectively.

Findings

0.7% improvement in IDF1 accuracy on MOT17 dataset

Better performance in fixed-camera scenarios

Reduces occlusion-related errors

Abstract

In this work, we consider data association problems involving multi-object tracking (MOT). In particular, we address the challenges arising from object occlusions. We propose a framework called approximate dynamic programming track (ADPTrack), which applies dynamic programming principles to improve an existing method called the base heuristic. Given a set of tracks and the next target frame, the base heuristic extends the tracks by matching them to the objects of this target frame directly. In contrast, ADPTrack first processes a few subsequent frames and applies the base heuristic starting from the next target frame to obtain tentative tracks. It then leverages the tentative tracks to match the objects of the target frame. This tends to reduce the occlusion-based errors and leads to an improvement over the base heuristic. When tested on the MOT17 video dataset, the proposed method demonstrates a 0.7% improvement in the association accuracy (IDF1 metric) over a state-of-the-art method that is used as the base heuristic. It also obtains improvements with respect to all the other standard metrics. Empirically, we found that the improvements are particularly pronounced in scenarios where the video data is obtained by fixed-position cameras.