SPOT!: 多CCTV动态目标跟踪的映射引导LLM代理
SPOT!: Map-Guided LLM Agent for Unsupervised Multi-CCTV Dynamic Object Tracking
AI总结：
SPOT通过映射引导LLM代理在多CCTV环境中实现无先验训练的动态目标跟踪，有效解决盲区导致的轨迹丢失问题。
摘要：
基于CCTV的车辆跟踪系统在连续连接多个摄像头环境中的同一车辆轨迹时面临结构性限制。特别是由于CCTV之间的间隔和有限的视野（FOV），导致盲区出现，从而引发目标ID切换和轨迹丢失，进而降低实时路径预测的可靠性。本文提出SPOT（空间预测过轨迹），一种能够跟踪车辆的映射引导LLM代理，即使在多CCTV环境的盲区中也能实现无先验训练的跟踪。所提出的方法将道路结构（路径点）和CCTV布置信息基于2D空间坐标表示为文档，并通过分块技术组织它们以实现实时查询和推理。此外，它利用CCTV图像中观察到的对象的相对位置和FOV信息，将车辆的位置转换为实际世界坐标系。通过结合地图空间信息与车辆的运动方向、速度和驾驶模式，在交汇点级别进行束搜索，以推导出车辆在盲区后最可能进入的候选CCTV位置。基于CARLA模拟器在虚拟城市环境中的实验结果证实，所提出的方法能够准确预测盲区部分中下一个出现的CCTV，比现有技术更有效地维持连续车辆轨迹。
英文摘要：
CCTV-based vehicle tracking systems face structural limitations in continuously connecting the trajectories of the same vehicle across multiple camera environments. In particular, blind spots occur due to the intervals between CCTVs and limited Fields of View (FOV), which leads to object ID switching and trajectory loss, thereby reducing the reliability of real-time path prediction. This paper proposes SPOT (Spatial Prediction Over Trajectories), a map-guided LLM agent capable of tracking vehicles even in blind spots of multi-CCTV environments without prior training. The proposed method represents road structures (Waypoints) and CCTV placement information as documents based on 2D spatial coordinates and organizes them through chunking techniques to enable real-time querying and inference. Furthermore, it transforms the vehicle's position into the actual world coordinate system using the relative position and FOV information of objects observed in CCTV images. By combining map spatial information with the vehicle's moving direction, speed, and driving patterns, a beam search is performed at the intersection level to derive candidate CCTV locations where the vehicle is most likely to enter after the blind spot. Experimental results based on the CARLA simulator in a virtual city environment confirmed that the proposed method accurately predicts the next appearing CCTV even in blind spot sections, maintaining continuous vehicle trajectories more effectively than existing techniques.
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。
