Enhancing In-vehicle Multiple Object Tracking Systems with Embeddable Ising Machines

TL;DR

This paper presents a real-time in-vehicle multiple object tracking system that uses embeddable Ising machines based on simulated bifurcation to solve complex assignment problems, improving tracking through occlusions.

Contribution

The paper introduces a novel in-vehicle tracking system utilizing quantum-inspired Ising machines for flexible object association, enabling real-time performance during occlusion events.

Findings

Achieved 23 frames per second processing speed.

Successfully handled long-term occlusion events.

Demonstrated real-time system performance on vehicle-mountable hardware.

Abstract

A cognitive function of tracking multiple objects, needed in autonomous mobile vehicles, comprises object detection and their temporal association. While great progress owing to machine learning has been recently seen for elaborating the similarity matrix between the objects that have been recognized and the objects detected in a current video frame, less for the assignment problem that finally determines the temporal association, which is a combinatorial optimization problem. Here we show an in-vehicle multiple object tracking system with a flexible assignment function for tracking through multiple long-term occlusion events. To solve the flexible assignment problem formulated as a nondeterministic polynomial time-hard problem, the system relies on an embeddable Ising machine based on a quantum-inspired algorithm called simulated bifurcation. Using a vehicle-mountable computing platform, we demonstrate a realtime system-wide throughput (23 frames per second on average) with the enhanced functionality.