Confidence Trigger Detection: Accelerating Real-time Tracking-by-detection Systems
This work addresses the problem of computational efficiency for real-time tracking systems, particularly in resource-constrained environments, though it appears incremental as it builds on existing tracking-by-detection methods.
The paper tackled the challenge of balancing speed and accuracy in real-time object tracking by proposing Confidence-Triggered Detection (CTD), which bypasses detection in similar frames using tracker confidence scores, resulting in enhanced tracking speed while preserving accuracy and surpassing existing algorithms.
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.