ArticleNature communications2025
Enhancing vehicle-mountable multiple object tracking systems with embeddable Ising machines.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The trial behind it
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Who cites it
3 citing papers in PubMed.
- Real-time black-box optimization for dynamic discrete environments using embedded Ising machines.Nature communications · 2026Article
- Machine learning-assisted high-speed combinatorial optimization with Ising machines for dynamically changing problems.Nature communications · 2026Article
- Enhancing multiple object tracking accuracy via quantum annealing.Scientific reports · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The cognitive function of tracking multiple objects, essential for autonomous mobile vehicles or autonomous robots, involves object detection and their temporal associations. While significant progress has recently been made in machine learning to elaborate the similarity matrix between the objects that have been recognized and the objects detected in the current video frame, less progress has been made on the assignment problem that ultimately determines temporal associations, which is a combinatorial optimization problem. Here we show a vehicle-mountable 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 embedded Ising machine based on a quantum-inspired algorithm called simulated bifurcation. Using a vehicle-mountable computing platform, we demonstrate real-time system-wide throughput of more than 20 frames per second with the enhanced functionality.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.