This paper proposes a visual multi-object tracking method that jointly employs stochastic and deterministic mechanisms to ensure identifier consistency for unknown and time-varying target numbers under nonlinear dynamics. A stochastic particle filter addresses nonlinear dynamics and non-Gaussian noise, with support from particle swarm optimization (PSO) to guide particles toward state distribution modes and mitigate divergence through proposed fitness measures incorporating motion consistency, appearance similarity, and social-interaction cues with neighboring targets. Deterministic association further enforces identifier consistency via a proposed cost matrix incorporating spatial consistency between particles and current detections, detection confidences, and track penalties. Subsequently, a novel scheme is proposed for the smooth updating of target states while preserving their identities, particularly for weak tracks during interactions with other targets and prolonged occlusions. Moreover, velocity regression over past states provides trend-seed velocities, enhancing particle sampling and state updates. The proposed tracker is designed to operate flexibly for both pre-recorded videos and camera live streams, where future frames are unavailable. Experimental results confirm superior performance compared to state-of-the-art trackers. The source-code reference implementations of both the proposed method and compared-trackers are provided on GitHub: https://github.com/SDU-VelKoTek/GenTrack2

翻译：本文提出了一种视觉多目标跟踪方法，该方法联合采用随机与确定性机制，以确保在非线性动力学下针对未知且时变的目标数量保持标识符一致性。随机粒子滤波器处理非线性动力学与非高斯噪声，并借助粒子群优化（PSO）引导粒子朝向状态分布模态，通过融合运动一致性、外观相似性及与邻近目标的社会交互线索的适应度度量来缓解发散。确定性关联进一步通过提出的成本矩阵（包含粒子与当前检测间的空间一致性、检测置信度及轨迹惩罚）强化标识符一致性。随后，提出了一种新颖方案，用于平滑更新目标状态同时保持其身份，尤其针对与其他目标交互期间及长时间遮挡下的弱轨迹。此外，基于历史状态的速率回归提供趋势种子速率，以增强粒子采样与状态更新。所提出的跟踪器设计为可灵活适用于预录制视频和相机实时流（其中未来帧不可用）。实验结果证实了相较于最先进跟踪器的优越性能。所提方法及对比跟踪器的源代码参考实现已在GitHub上提供：https://github.com/SDU-VelKoTek/GenTrack2