Multi-Object Tracking and Identification over Sets

TL;DR

This paper introduces a set-based multi-object tracking and identification method that effectively handles noisy perceptions and outperforms current state-of-the-art algorithms in benchmark tests.

Contribution

It proposes a novel set-based formulation for multi-object tracking and identification, utilizing expectation-maximization to avoid explicit observation-to-object association.

Findings

Outperforms state-of-the-art methods on PETS dataset

Handles noisy and incomplete perception effectively

Avoids direct observation-to-object association

Abstract

The ability for an autonomous agent or robot to track and identify potentially multiple objects in a dynamic environment is essential for many applications, such as automated surveillance, traffic monitoring, human-robot interaction, etc. The main challenge is due to the noisy and incomplete perception including inevitable false negative and false positive errors from a low-level detector. In this paper, we propose a novel multi-object tracking and identification over sets approach to address this challenge. We define joint states and observations both as finite sets, and develop motion and observation functions accordingly. The object identification problem is then formulated and solved by using expectation-maximization methods. The set formulation enables us to avoid directly performing observation-to-object association. We empirically confirm that the overall algorithm outperforms the state-of-the-art in a popular PETS dataset.