MMLT: Efficient object tracking through machine learning-based meta-learning

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F25%3A10257833" target="_blank" >RIV/61989100:27240/25:10257833 - isvavai.cz</a>

Result on the web

<a href="https://www.sciencedirect.com/science/article/pii/S259012302500845X" target="_blank" >https://www.sciencedirect.com/science/article/pii/S259012302500845X</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.rineng.2025.104768" target="_blank" >10.1016/j.rineng.2025.104768</a>

Result language

angličtina

Original language name

MMLT: Efficient object tracking through machine learning-based meta-learning

Original language description

Object tracking in computer vision is challenging due to the complexities of spatiotemporal data, including occlusions, abrupt motion changes, temporal coherence, object identity maintenance, and scale variations. While Deep learning algorithms address these challenges, however, they typically require significant computational resources, exhibit high complexity, and demand large amounts of training data. To mitigate these constraints, various strategies—such as lightweight neural networks and model compression techniques—have been developed. In contrast, traditional machine learning and classical computer vision methods like Kernelized Correlation Filters (KCF), Tracking, Learning, and Detection (TLD), and Bootstrap Aggregating (BOOSTING), lacks reliability in performance. This paper introduces a machine learning-based approach to one-shot meta-learning for more efficient object tracking. The proposed hybrid model refines predictions from traditional tracking methods using machine learning to enhance performance. This method offers lower computational complexity, requires fewer resources, and needs minimal training data. The proposed model achieves a frame rate of 13.74 FPS, which, while below real-time performance, maintains a trade-off between accuracy and computational efficiency, making it suitable for applications with moderate latency tolerance. The meta-learning-based approach is evaluated on the VOT2017, OTB50, and GOT10K datasets, outperforming existing deep-learning models in most cases. On the OTB50 dataset, the model with XGBoost achieved an OTE of 9.12%, a precision of 92.0%, and a success rate of 86.0%. On the GOT10k dataset, the model with a Decision Tree meta-learner recorded an average overlap of 62.2%, with success rates of 57.9% at 0.5 IoU and 52.0% at 0.75 IoU. On the VoT dataset, the Decision Tree meta-learner attained 79.0% EAO, 88.0% accuracy, and a robustness score of 20.0%. © 2025 The Authors

Czech name

—

Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

20200 - Electrical engineering, Electronic engineering, Information engineering

Project

—

Continuities

S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Results in Engineering

ISSN

2590-1230

e-ISSN

2590-1230

Volume of the periodical

26

Issue of the periodical within the volume

2025

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

13

Pages from-to

nestránkováno

UT code for WoS article

001468958100001

EID of the result in the Scopus database

2-s2.0-105002018024