Confidence Trigger Detection: Accelerating Real-time Tracking-by-detection Systems

• URL: http://arxiv.org/abs/1902.00615v6
• Date: Wed, 13 Nov 2024 18:32:53 GMT
• Title: Confidence Trigger Detection: Accelerating Real-time Tracking-by-detection Systems
• Authors: Zhicheng Ding, Zhixin Lai, Siyang Li, Panfeng Li, Qikai Yang, Edward Wong,
• Abstract summary: Confidence-Triggered Detection (CTD) is an innovative approach that strategically bypasses object detection for frames closely resembling intermediate states. CTD not only enhances tracking speed but also preserves accuracy, surpassing existing tracking algorithms. Our experiments underscore the robustness and versatility of the CTD framework, demonstrating its potential to enable real-time tracking in resource-constrained environments.
• Score: 1.6037469030022993
• License:
• Abstract: Real-time object tracking necessitates a delicate balance between speed and accuracy, a challenge exacerbated by the computational demands of deep learning methods. In this paper, we propose Confidence-Triggered Detection (CTD), an innovative approach that strategically bypasses object detection for frames closely resembling intermediate states, leveraging tracker confidence scores. CTD not only enhances tracking speed but also preserves accuracy, surpassing existing tracking algorithms. Through extensive evaluation across various tracker confidence thresholds, we identify an optimal trade-off between tracking speed and accuracy, providing crucial insights for parameter fine-tuning and enhancing CTD's practicality in real-world scenarios. Our experiments across diverse detection models underscore the robustness and versatility of the CTD framework, demonstrating its potential to enable real-time tracking in resource-constrained environments.

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