Open Access

ARTICLE

Cue-Tracker: Integrating Deep Appearance Features and Spatial Cues for Multi-Object Tracking

Sheeba Razzaq^1,*, Majid Iqbal Khan²

1 Department of Computer Science & Information Technology, University of Kotli Azad Jammu and Kashmir, Kotli, 11100, Pakistan
2 Department of Computer Science, COMSATS University Islamabad, Islamabad, 45550, Pakistan

* Corresponding Author: Sheeba Razzaq. Email:

(This article belongs to the Special Issue: Computer Vision and Image Processing: Feature Selection, Image Enhancement and Recognition)

Computers, Materials & Continua 2025, 85(3), 5377-5398. https://doi.org/10.32604/cmc.2025.068539

Received 31 May 2025; Accepted 19 August 2025; Issue published 23 October 2025

Abstract

Multi-Object Tracking (MOT) represents a fundamental but computationally demanding task in computer vision, with particular challenges arising in occluded and densely populated environments. While contemporary tracking systems have demonstrated considerable progress, persistent limitations—notably frequent occlusion-induced identity switches and tracking inaccuracies—continue to impede reliable real-world deployment. This work introduces an advanced tracking framework that enhances association robustness through a two-stage matching paradigm combining spatial and appearance features. Proposed framework employs: (1) a Height Modulated and Scale Adaptive Spatial Intersection-over-Union (HMSIoU) metric for improved spatial correspondence estimation across variable object scales and partial occlusions; (2) a feature extraction module generating discriminative appearance descriptors for identity maintenance; and (3) a recovery association mechanism for refining matches between unassociated tracks and detections. Comprehensive evaluation on standard MOT17 and MOT20 benchmarks demonstrates significant improvements in tracking consistency, with state-of-the-art performance across key metrics including HOTA (64), MOTA (80.7), IDF1 (79.8), and IDs (1379). These results substantiate the efficacy of our Cue-Tracker framework in complex real-world scenarios characterized by occlusions and crowd interactions.

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Keywords

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