Measurement-wise Occlusion in Multi-object Tracking

TL;DR

This paper introduces measurement-wise occlusion as a novel abstraction for multi-object tracking, which allows for more flexible modeling of occlusion effects and aligns with common lidar tracking approximations.

Contribution

It formalizes measurement-wise occlusion, a new approach that extends probabilistic multi-object tracking to better handle occlusion scenarios.

Findings

Measurement-wise occlusion fits into existing tracking algorithms with looser assumptions.

It naturally derives a popular lidar tracking approximation.

Demonstrated effectiveness in visual tracking in image space.

Abstract

Handling object interaction is a fundamental challenge in practical multi-object tracking, even for simple interactive effects such as one object temporarily occluding another. We formalize the problem of occlusion in tracking with two different abstractions. In object-wise occlusion, objects that are occluded by other objects do not generate measurements. In measurement-wise occlusion, a previously unstudied approach, all objects may generate measurements but some measurements may be occluded by others. While the relative validity of each abstraction depends on the situation and sensor, measurement-wise occlusion fits into probabilistic multi-object tracking algorithms with much looser assumptions on object interaction. Its value is demonstrated by showing that it naturally derives a popular approximation for lidar tracking, and by an example of visual tracking in image space.