Evidence map›Paper›PMID 40624138›Full record

ArticleScientific reports2025

Enhancing multiple object tracking accuracy via quantum annealing.

Yasuyuki Ihara

Abstract read
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

1 author.

Yasuyuki IharaInnovation Laboratories, NEC Solution Innovators, Ltd., 1-18-7 Shinkiba, Koto-ku, Tokyo, 136-8627, Japan. ihara_mxk@nec.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multiple object tracking (MOT), a key task in image recognition, poses a persistent challenge in balancing processing speed and tracking accuracy. This study presents a novel approach that leverages quantum annealing (QA) to expedite computation speed, while improving tracking accuracy through the ensemble processing of object tracking methods. A method to improve the matching integration process is also proposed. By utilizing the sequential nature of MOT, this study further augments the tracking method via reverse annealing. Experimental validation confirms the maintenance of high accuracy with an annealing time of a mere 3 μs per tracking process. Notably, the time-to-solution achieved reductions of over 99% compared to conventional QA implementations. The proposed method holds significant potential for real-time MOT applications, including traffic flow measurement for urban traffic light control, collision prediction for autonomous robots and vehicles, and management of products mass-produced in factories.

Indexed as

Combinatorial optimization problemImage recognitionMultiple object trackingQuantum annealingReverse annealing

Identifiers

PMID40624138
PMCPMC12234660

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

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.