SWTrack：多假设滑动窗口三维多目标跟踪
SWTrack: Multiple Hypothesis Sliding Window 3D Multi-Object Tracking
AI总结：
SWTrack提出一种基于多假设滑动窗口的3D多目标跟踪方法，通过批处理多帧数据并利用图优化解决关联问题，在NuScenes数据集上实现了更准确的跟踪性能。
摘要：
现代机器人系统需要在密集的动态环境中运行，这要求高度准确的实时轨迹识别和估计。对于三维多目标跟踪，近期的方法使用贪婪关联递归处理单个测量帧，在模糊的关联决策中容易出错。我们的方法，滑动窗口跟踪器（SWTrack），通过批处理多帧传感器数据，实现更准确的关联和状态估计，同时能够在线实时运行。通过在时间滑动窗口内评估所有可能的轨迹假设，识别出最可能的轨迹关联。我们提出了一种新颖的图优化方法来解决多维分配问题，引入提升图边以处理漏检，并强制图稀疏性以保持实时效率。我们在NuScenes自动驾驶数据集上评估了我们的SWTrack实现，以展示改进的跟踪性能。
英文摘要：
Modern robotic systems are required to operate in dense dynamic environments, requiring highly accurate real-time track identification and estimation. For 3D multi-object tracking, recent approaches process a single measurement frame recursively with greedy association and are prone to errors in ambiguous association decisions. Our method, Sliding Window Tracker (SWTrack), yields more accurate association and state estimation by batch processing many frames of sensor data while being capable of running online in real-time. The most probable track associations are identified by evaluating all possible track hypotheses across the temporal sliding window. A novel graph optimization approach is formulated to solve the multidimensional assignment problem with lifted graph edges introduced to account for missed detections and graph sparsity enforced to retain real-time efficiency. We evaluate our SWTrack implementation$^{2}$ on the NuScenes autonomous driving dataset to demonstrate improved tracking performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
