GPU-GLMB: Assessing the Scalability of GPU-Accelerated Multi-Hypothesis Tracking

TL;DR

This paper introduces a GPU-accelerated variant of the GLMB multi-target tracking filter that improves scalability and supports multiple detections per object, enabling efficient deployment in distributed sensor networks.

Contribution

We propose a modified GLMB filter that breaks inter-detection dependencies, allowing for parallel GPU implementation and better scalability in multi-object tracking scenarios.

Findings

GPU implementation significantly improves run time scalability.

Supports multiple detections per object from the same sensor.

Enables deployment in distributed sensor networks.

Abstract

Much recent research on multi-target tracking has focused on multi-hypothesis approaches leveraging random finite sets. Of particular interest are labeled random finite set methods that maintain temporally coherent labels for each object. While these methods enjoy important theoretical properties as closed-form solutions to the multi-target Bayes filter, the maintenance of multiple hypotheses under the standard measurement model is highly computationally expensive, even when hypothesis pruning approximations are applied. In this work, we focus on the Generalized Labeled Multi-Bernoulli (GLMB) filter as an example of this class of methods. We investigate a variant of the filter that allows multiple detections per object from the same sensor, a critical capability when deploying tracking in the context of distributed networks of machine learning-based virtual sensors. We show that this breaks the inter-detection dependencies in the filter updates of the standard GLMB filter, allowing updates with significantly improved parallel scalability and enabling efficient deployment on GPU hardware. We report the results of a preliminary analysis of a GPU-accelerated implementation of our proposed GLMB tracker, with a focus on run time scalability with respect to the number of objects and the maximum number of retained hypotheses.