A Lightweight SMC-SORT Based Method for On-Device Real-Time Multiple Object Tracking 


Vol. 14,  No. 11, pp. 960-966, Nov.  2025
https://doi.org/10.3745/TKIPS.2025.14.11.960


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  Abstract

Efficient multiple object tracking (MOT) in video is crucial for applications such as autonomous driving and video surveillance. Although recent advances in deep learning have popularized the tracking-by-detection paradigm, coupling high-accuracy detectors with trackers often hinders real-time operation on resource-constrained edge devices. This paper proposes a MOT method that attains real-time performance on edge platforms by combining a non–deep-learning Gaussian Mixture Model (GMM)-based background subtraction detector with a newly designed lightweight tracker. Building on the simplicity of SORT—i.e., a Kalman filter with Hungarian assignment—the proposed tracker introduces update and error-handling mechanisms tailored to the characteristics of background-subtracted detections. As a result, object identities are preserved through short-term occlusions and temporary stops, improving temporal continuity. Experiments demonstrate that the method achieves real-time processing with markedly lower computational load than deep learning pipelines such as YOLOv5s+DeepSORT, while delivering improved accuracy over SORT. Evaluations on Raspberry Pi 5 and Jetson AGX Orin in both CPU and GPU modes further confirm that the proposed approach offers an effective balance of efficiency and accuracy for resource-constrained edge environments.

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[IEEE Style]

L. W. Kyoung and K. Dongho, "A Lightweight SMC-SORT Based Method for On-Device Real-Time Multiple Object Tracking," The Transactions of the Korea Information Processing Society, vol. 14, no. 11, pp. 960-966, 2025. DOI: https://doi.org/10.3745/TKIPS.2025.14.11.960.

[ACM Style]

Lee Won Kyoung and Kim Dongho. 2025. A Lightweight SMC-SORT Based Method for On-Device Real-Time Multiple Object Tracking. The Transactions of the Korea Information Processing Society, 14, 11, (2025), 960-966. DOI: https://doi.org/10.3745/TKIPS.2025.14.11.960.

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