融合随机与确定性机制，提升复杂场景下多目标跟踪的稳定性与一致性。
GenTrack2: An Improved Hybrid Approach for Multi-Object Tracking
- 结合粒子滤波与粒子群优化，利用运动、外观和社会交互线索增强状态估计
- 通过新成本矩阵实现身份一致关联，有效处理遮挡与目标交互问题
- 支持实时视频流与离线视频，适用于目标数量动态变化的场景
本文提出一种视觉多目标跟踪方法，联合使用随机与确定性机制，在非线性动态和未知目标数量变化条件下保证标识一致性。采用粒子滤波应对非线性动态与非高斯噪声，并引入粒子群优化（PSO）引导粒子向状态分布模态集中，通过融合运动一致性、外观相似性与邻近目标社交交互线索的适应度函数抑制发散。确定性关联进一步通过新提出的成本矩阵强化身份一致性，该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项。随后提出一种新颖的状态平滑更新策略，特别在目标交互和长时间遮挡时保持弱轨迹的身份连续性。同时，基于历史状态进行速度回归，生成趋势初值速度，提升粒子采样与状态更新效果。所提追踪器可灵活应用于预录视频与摄像头实时流，无需未来帧信息。实验结果表明性能优于现有先进追踪器。源码与对比方法实现均已在GitHub公开：https://github.com/SDU-VelKoTek/GenTrack2
原文摘要 · Abstract (English)
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
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