Multiple Object Tracking in Urban Traffic Scenes with a Multiclass Object Detector

TL;DR

This paper explores the use of a multiclass deep learning object detector for multiple object tracking in urban traffic scenes, highlighting that object labels can improve tracking but detector outputs may be unreliable.

Contribution

It investigates the integration of classification label information from modern detectors into MOT, demonstrating potential benefits and limitations in urban traffic scenarios.

Findings

Object labels enhance tracking accuracy.

Detector outputs can be unreliable in complex scenes.

Multiclass detection contributes to better object association.

Abstract

Multiple object tracking (MOT) in urban traffic aims to produce the trajectories of the different road users that move across the field of view with different directions and speeds and that can have varying appearances and sizes. Occlusions and interactions among the different objects are expected and common due to the nature of urban road traffic. In this work, a tracking framework employing classification label information from a deep learning detection approach is used for associating the different objects, in addition to object position and appearances. We want to investigate the performance of a modern multiclass object detector for the MOT task in traffic scenes. Results show that the object labels improve tracking performance, but that the output of object detectors are not always reliable.