GenTrack用粒子群优化实现多目标跟踪，抗干扰强且保持身份一致。
GenTrack: A New Generation of Multi-Object Tracking
- 混合随机与确定性策略，应对目标数变化和非线性运动。
- 粒子群优化结合社会交互建模，在弱检测下仍能准确追踪。
- 开源三版本代码，适合研究者快速复现与对比实验。
本文提出一种新型多目标跟踪方法GenTrack，主要贡献包括：首先，采用混合跟踪策略，结合随机与确定性方式，有效处理未知且时变的目标数量，尤其在保持目标身份一致性及管理非线性动态方面表现优异；其次，利用粒子群优化（PSO）并设计新适应度函数，引导随机粒子向目标分布模式收敛，即使在检测弱且噪声大的情况下也能实现有效跟踪；第三，引入目标间社会交互建模，增强PSO引导的粒子性能，并提升强匹配与弱未匹配轨迹的连续更新能力，显著减少遮挡情况下的身份切换与轨迹丢失；第四，构建基于空间一致性、外观特征、检测置信度、轨迹惩罚与社交得分的综合性状态与观测模型，重新定义视觉多目标跟踪基线，实现系统高效的目标更新；第五，首次公开提供源码参考实现，依赖少，包含GenTrack Simple、Strengthen、Super三个变体，便于灵活复现。实验表明，GenTrack在标准基准与真实场景中均优于现有最先进追踪器，且提供完整基线与对比实现。未来工作方向亦被讨论。源码已发布于GitHub：https://github.com/SDU-VelKoTek/GenTrack
原文摘要 · Abstract (English)
This paper introduces a novel multi-object tracking (MOT) method, dubbed GenTrack, whose main contributions include: first-a hybrid tracking approach employing both stochastic and deterministic manners to robustly handle unknown and time-varying numbers of targets, particularly in maintaining target identity (ID) consistency and managing nonlinear dynamics, second-leveraging particle swarm optimization (PSO) with some proposed fitness measures to guide stochastic particles toward their target distribution modes, enabling effective tracking even with weak and noisy object detectors, third-integration of social interactions among targets to enhance PSO-guided particles as well as improve continuous updates of both strong (matched) and weak (unmatched) tracks, thereby reducing ID switches and track loss, especially during occlusions, fourth-a GenTrack-based redefined visual MOT baseline incorporating a comprehensive state and observation model based on space consistency, appearance, detection confidence, track penalties, and social scores for systematic and efficient target updates, and five-the first ever publicly available source-code reference implementation with minimal dependencies, featuring three variants, including GenTrack Simple, Strengthen, and Super, facilitating flexible reimplementation. Experimental results have shown that GenTrack provides superior performance on standard benchmarks and real-world scenarios compared to state-of-the-art trackers, with integrated implementations of baselines for fair comparison. Potential directions for future work are also discussed. The source-code reference implementations of both the proposed method and compared-trackers are provided on GitHub: https://github.com/SDU-VelKoTek/GenTrack
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