使用一组最小成本技术和深度判别相关滤波器改进基于无人机的 crowd monitoring 中的轨迹连续性
Improving trajectory continuity in drone-based crowd monitoring using a set of minimal-cost techniques and deep discriminative correlation filters
AI总结：
本文提出一种基于 SORT 框架的面向点在线跟踪算法，结合三种低成本技术和 DDCF 滤波器，有效提升了无人机 crowd monitoring 的轨迹连续性与计数可靠性。
摘要：
基于无人机的 crowd monitoring 是监控、公共安全和事件管理应用的关键技术。然而，保持跟踪的连续性和一致性仍然是一个重大挑战。传统的 detection-assignment tracking 方法难以处理假阳性、假阴性和频繁的身份切换，导致计数精度下降，并使深入分析变得不可能。本文引入了一种面向点的在线跟踪算法，该算法提高了基于无人机的 crowd monitoring 中的轨迹连续性和计数可靠性。我们的方法建立在 Simple Online and Real-time Tracking (SORT) 框架之上，用点距离度量替换了原始的边界框分配。该算法通过三种低成本技术得到了增强：camera motion compensation、高度感知分配和基于分类的轨迹验证。此外，集成了 Deep Discriminative Correlation Filters (DDCF)，该滤波器通过神经网络资源共享重用来自定位算法的空间特征图以提高计算效率，通过减少噪声和处理漏检来细化对象跟踪。所提出的方法在 DroneCrowd 和新共享的 UP-COUNT-TRACK 数据集上进行了评估，在跟踪指标方面表现出显著改进，将计数误差分别降低至 23% 和 15%。结果还表明，身份切换显著减少，同时保持了高跟踪精度，优于基线在线跟踪器甚至离线贪婪优化方法。
英文摘要：
Drone-based crowd monitoring is the key technology for applications in surveillance, public safety, and event management. However, maintaining tracking continuity and consistency remains a significant challenge. Traditional detection-assignment tracking methods struggle with false positives, false negatives, and frequent identity switches, leading to degraded counting accuracy and making in-depth analysis impossible. This paper introduces a point-oriented online tracking algorithm that improves trajectory continuity and counting reliability in drone-based crowd monitoring. Our method builds on the Simple Online and Real-time Tracking (SORT) framework, replacing the original bounding-box assignment with a point-distance metric. The algorithm is enhanced with three cost-effective techniques: camera motion compensation, altitude-aware assignment, and classification-based trajectory validation. Further, Deep Discriminative Correlation Filters (DDCF) that re-use spatial feature maps from localisation algorithms for increased computational efficiency through neural network resource sharing are integrated to refine object tracking by reducing noise and handling missed detections. The proposed method is evaluated on the DroneCrowd and newly shared UP-COUNT-TRACK datasets, demonstrating substantial improvements in tracking metrics, reducing counting errors to 23% and 15%, respectively. The results also indicate a significant reduction of identity switches while maintaining high tracking accuracy, outperforming baseline online trackers and even an offline greedy optimisation method.
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。
