Tracking Road Users using Constraint Programming

TL;DR

This paper introduces a constraint programming approach for data association in multiple object tracking, improving efficiency and accuracy in urban road user tracking by leveraging simple features and constraints.

Contribution

The paper presents a novel CP-based method for data association in MOT that outperforms graph-based methods and handles combinatorial complexity effectively.

Findings

Outperforms top methods on UA-DETRAC benchmark

Efficiently handles multiple frames with simple features

Improves data association accuracy in urban scenes

Abstract

In this paper, we aim at improving the tracking of road users in urban scenes. We present a constraint programming (CP) approach for the data association phase found in the tracking-by-detection paradigm of the multiple object tracking (MOT) problem. Such an approach can solve the data association problem more efficiently than graph-based methods and can handle better the combinatorial explosion occurring when multiple frames are analyzed. Because our focus is on the data association problem, our MOT method only uses simple image features, which are the center position and color of detections for each frame. Constraints are defined on these two features and on the general MOT problem. For example, we enforce color appearance preservation over trajectories and constrain the extent of motion between frames. Filtering layers are used in order to eliminate detection candidates before using CP and to remove dummy trajectories produced by the CP solver. Our proposed method was tested on a motorized vehicles tracking dataset and produces results that outperform the top methods of the UA-DETRAC benchmark.