用语义和遮挡信息改进目标跟踪初始化，提升复杂场景下的准确性。
A Semantic and Occlusion-Aware GM-PHD Filter
- 引入融合语义与遮挡信息的新生模型，改进目标出现位置预测
- 在KITTI数据集上减少70%以上遮挡场景下的初始化延迟
- 适合自动驾驶中高密度交通场景的目标跟踪研究者
本文提出一种新出生模型，结合深度学习提取的语义信息，构建具备遮挡感知能力的高斯混合概率假设密度（GM-PHD）滤波器。与以往依赖简单或均匀假设的方法不同，所提的语义-遮挡感知（S-OA）出生模型显式考虑遮挡区域，并利用环境语义信息定义初始化项，从而更准确地刻画新目标可能出现的位置，提升复杂高密度驾驶场景下的跟踪性能。方法通过蒙特卡洛仿真和KITTI数据集实验验证，评估指标包括首次检测到轨迹启动的延迟、平均绝对基数误差及最优子模式分配（OSPA）距离。结果表明，该模型在遮挡密集场景下显著降低初始化延迟，在约70%的情况下达到或超过最强基线表现。同时提供了出生模型权重的敏感性分析。整体表明，将遮挡推理与语义先验融入贝叶斯跟踪框架对自动驾驶具有显著价值。
原文摘要 · Abstract (English)
This paper proposes a new birth model including semantic information derived from deep learning to create an occlusion-aware Gaussian Mixture Probability Hypothesis Density (GM-PHD) filter. Unlike prior approaches that rely on simplistic or uniform assumptions, the proposed Semantic-Occlusion Aware (S-OA) birth model defines initialization terms by explicitly considering regions of occlusion and by leveraging semantic information about the environment. This enables the filter to accurately represent where new objects are more likely to appear, thereby improving tracking performance in complex and high-density driving scenarios. The method is evaluated through Monte Carlo simulations and experiments on the KITTI dataset. Performance is assessed by measuring the latency between first detection and track initiation, along with the mean absolute cardinality error and the Optimal Subpattern Assignment (OSPA) metric. Results demonstrate that the S-OA birth model reduces initialization delay in occlusion-heavy settings, matching or outperforming the strongest baseline in approximately 70% of cases. A sensitivity analysis of birth model weights is also provided. Overall, the findings underscore the benefits of integrating occlusion reasoning and semantic priors into Bayesian tracking frameworks for autonomous driving.
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