Occlusion-Aware Multi-Object Tracking via Expected Probability of Detection

TL;DR

This paper introduces an occlusion-aware multi-object tracking method that models detection probabilities considering object visibility and occlusions, improving tracking accuracy in cluttered environments.

Contribution

It develops a principled tracking approach using expected detection probabilities conditioned on object existence, accounting for occlusions systematically.

Findings

Demonstrated effectiveness with a visual tracking application.

Utilizes the multi-Bernoulli mixture (MBM) filter with marks.

Systematically accounts for uncertainties due to occlusions.

Abstract

This paper addresses multi-object systems, where objects may occlude one another relative to the sensor. The standard point-object model for detection-based sensors is enhanced so that the probability of detection considers the presence of all objects. A principled tracking method is derived, assigning each object an expected probability of detection, where the expectation is taken over the reduced Palm density, which means conditionally on the object's existence. The assigned probability thus considers the object's visibility relative to the sensor, under the presence of other objects. Unlike existing methods, the proposed method systematically accounts for uncertainties related to all objects in a clear and manageable way. The method is demonstrated through a visual tracking application using the multi-Bernoulli mixture (MBM) filter with marks.