Interaction-Aware Labeled Multi-Bernoulli Filter

TL;DR

This paper introduces an interaction-aware extension to the labeled multi-Bernoulli filter for multi-object tracking, effectively modeling target interactions to improve tracking accuracy in complex scenarios.

Contribution

It presents a novel method to incorporate target interactions into the prediction step of an RFS-based multi-target filter, enhancing tracking performance in real-world applications.

Findings

Significant performance improvement over standard LMB filter.

Effective in tracking coordinated swarms and vehicles.

Validated on complex vehicle tracking dataset.

Abstract

Tracking multiple objects through time is an important part of an intelligent transportation system. Random finite set (RFS)-based filters are one of the emerging techniques for tracking multiple objects. In multi-object tracking (MOT), a common assumption is that each object is moving independent of its surroundings. But in many real-world applications, target objects interact with one another and the environment. Such interactions, when considered for tracking, are usually modeled by an interactive motion model which is application specific. In this paper, we present a novel approach to incorporate target interactions within the prediction step of an RFS-based multi-target filter, i.e. labeled multi-Bernoulli (LMB) filter. The method has been developed for two practical applications of tracking a coordinated swarm and vehicles. The method has been tested for a complex vehicle tracking dataset and compared with the LMB filter through the OSPA and OSPA$^{(2)}$ metrics. The results demonstrate that the proposed interaction-aware method depicts considerable performance enhancement over the LMB filter in terms of the selected metrics.