An Overview of Machine Learning Methods for Multiple Target Tracking

Abstract

Traditional multiple target tracking (MTT) algorithms are model-based. Target and sensor models are used to associate measurements, perform track filtering, score possible associations, and find the best association hypothesis. Recent advances in machine learning (ML) have resulted in data-driven model-free methods for MTT, especially in computer vision, where MTT is called multiple object tracking (MOT). This paper presents an overview of ML methods for detection, track filtering, data association, and end-to-end MTT. It assesses the state-of-the-art and presents future research directions.