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

Autor:	Tatsumura, Kosuke, Hamakawa, Yohei, Yamasaki, Masaya, Oya, Koji, Fujimoto, Hiroshi
Rok vydání:	2024
Předmět:	Computer Science - Computer Vision and Pattern Recognition Computer Science - Emerging Technologies Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	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. Comment: 18 pages, 7 figures, 2 tables
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2410.14093 Zobrazit plný text záznamu View this record from Arxiv