置信度触发检测：加速基于检测的实时跟踪系统
Confidence Trigger Detection: Accelerating Real-time Tracking-by-detection Systems
AI总结：
本文提出置信度触发检测（CTD）方法，利用跟踪器置信度分数策略性跳过相似帧的目标检测，在保持准确性的同时提升速度，为资源受限环境下的实时跟踪提供了高效且鲁棒的解决方案。
摘要：
实时目标跟踪需要在速度和准确性之间取得微妙的平衡，而深度学习方法的计算需求加剧了这一挑战。本文提出置信度触发检测（CTD），这是一种创新方法，利用跟踪器置信度分数，策略性地跳过与中间状态高度相似的帧的目标检测。CTD不仅提高了跟踪速度，还保持了准确性，超越了现有的跟踪算法。通过对各种跟踪器置信度阈值进行广泛评估，我们确定了跟踪速度与准确性之间的最佳权衡，为参数微调提供了重要见解，并增强了CTD在真实场景中的实用性。我们在多种检测模型上的实验突显了CTD框架的鲁棒性和多功能性，证明了其在资源受限环境中实现实时跟踪的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
