Abstract
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.
Abstract (translated)
基于CCTV的车辆追踪系统在跨多个摄像头环境持续连接同一辆车轨迹时面临结构性限制。特别是，由于摄像头之间的间隔和有限的视野（FOV），会产生盲点区域，导致物体ID切换及轨迹丢失，从而降低实时路径预测的可靠性。本文提出了SPOT（轨迹上的空间预测）方法，这是一种基于地图引导的大规模语言模型代理，能够在没有前期训练的情况下，在多CCTV环境中的盲点区域内追踪车辆。所提出的方法将道路结构（路标）和CCTV安装信息以文档的形式表示在二维空间坐标上，并通过分块技术组织这些信息，以便于实时查询和推理。 此外，该方法利用CCTV图像中观察到的物体相对位置及视野信息，将车辆的位置转换为实际世界坐标系统。结合地图的空间信息、车辆行驶方向、速度以及驾驶模式，在交叉路口级别进行束搜索以确定车辆在盲点区域之后最有可能进入的候选CCTV位置。 基于虚拟城市环境中CARLA模拟器的实验结果表明，所提出的方法能够准确预测下一个出现的CCTV（即使在盲点区域内），比现有的技术更有效地保持连续的车辆轨迹。
URL
https://arxiv.org/abs/2512.20975
