AI 中文总结
本文提出GenTrack3框架，融合确定性与随机原理实现鲁棒在线多目标跟踪，引入集群感知轨迹-检测匹配方法，提升目标数量增加时的可扩展性并支持群组跟踪。
AI 中文摘要
多目标跟踪（MOT）需要在目标动态进出场景时维持其身份一致性。确定性方法（如带数据关联的检测跟踪）可生成可复现结果且计算效率高，但高度依赖运动模型，对噪声检测敏感易导致关联错误；随机方法则显式建模不确定性，能更好处理复杂非线性动态，不过需以更高计算复杂度和随机采样带来的变异性为代价。本文提出一种在线MOT框架，融合确定性与随机原理以在不确定性下实现鲁棒跟踪，还引入新颖的轨迹-检测匹配方法，在目标数量增加时提升可扩展性并支持群组跟踪。跟踪推理机制采用包含目标标识符、状态、速度、轨迹惩罚项及轨迹时长的轨迹片段（tracklet），支持系统化跟踪流程；每个目标关联一组随机粒子，以计算与检测结果的匹配代价。所提方法及基线跟踪器的参考实现可在GitHub获取：this https URL。
英文摘要
Multi-object tracking (MOT) involves maintaining consistent target identities as objects dynamically enter and leave a scene. Deterministic approaches, such as tracking-by-detection with data association, produce reproducible results and are computationally efficient, but they rely heavily on motion models and are sensitive to noisy detections that can lead to association errors. In contrast, stochastic methods explicitly model uncertainty and can better handle complex non-linear dynamics, albeit at the cost of increased computational complexity and variability arising from random sampling. This paper presents an online MOT framework that integrates deterministic and stochastic principles to achieve robust tracking under uncertainty. Furthermore, a novel track-to-detection matching approach is introduced to enhance scalability with increasing target numbers while supporting group tracking. The tracking inference mechanism employs a tracklet that includes identifiers, states, velocities, track penalties and track ages of targets, supporting a systematic tracking pipeline. Each target is associated with a stochastic particle set to compute the matching cost to detections. Reference implementations of the proposed approach and baseline trackers can be found on GitHub: https://github.com/SDU-VelKoTek/GenTrack3.
CommentsThe content of this paper was included in the full manuscript of GenTrack family which has been submitted to the journal for possible publication
