Recovering trajectories of all moving vehicles from urban-scale cameras is an attractive but challenging topic for massive video data management. Existing solutions frame it as an iterative image clustering problem. The snapshots from the same vehicle are grouped within a cluster, which is further refined according to the spatial-temporal attributes. However, these approaches exhibit expensive iterative clustering overhead and ineffective exploitation of spatial-temporal clues. Moreover, they are designed for batch processing, facing performance degradation when handling newly collected surveillance data. In this paper, we propose a novel joint representation clustering framework, which recovers trajectories from vehicle snapshots in an efficient and accurate fashion and is inherently suited for processing video streaming data. Technically, spatial-temporal features are explicitly extracted to construct the joint representation, eliminating the need for iterative refinement, which significantly reduces computational overhead. Furthermore, we present a simple yet effective clustering scheme with one-pass scan on joint representations to generate large-scale clusters. To mitigate the dependency on external data, a joint training method based on self-supervised learning is introduced. We conduct extensive experiments in both batch and streaming modes. The results show that in the batch mode, TRACER achieves a speedup of at least 2.3× and yields recovery F1-score improvements of 1.7% − 19.6%. In the streaming experimental setup, it achieves 1.1%−27.6% improvement in F1-score, and reduces the average snapshot processing time by up to 84.8%.

Path inference queries over urban-scale camera networks are essential for public safety applications, particularly in emergency scenarios such as suspect pursuit in violent crimes. The objective is to reconstruct the historical movement trajectory of a target object based on a given image query. However, conventional frameworks are often inefficient due to the high computational cost of exhaustive trajectory reconstruction. Moreover, achieving accurate path inference is challenging, as visual matching in real-world environments suffers from inherent uncertainties caused by occlusions, lighting variations, and viewpoint changes. To tackle these challenges, this paper introduces an innovative query-aware path inference framework in large-scale urban videos. The key idea is to eliminate unnecessary trajectory recovery by focusing only on query-relevant data, leveraging spatial-temporal patterns and high-order dependency modeling for accurate and efficient target path inference. We first build a similarity-based index to retrieve candidate vehicle snapshots matching the query, narrowing the search space for subsequent processing. Then we construct a probability motion graph that models the likelihood of transitions between candidate snapshots, effectively incorporating uncertainty and reducing the influence of visual noise. Finally, high-order spatial-temporal dependency constraints are introduced to ensure global consistency and enable robust trajectory extraction. To validate the effectiveness of our approach, we construct four benchmark datasets: two real-world medium-scale datasets collected from deployed urban surveillance systems, and two large-scale synthetic datasets simulating dense urban traffic. Extensive experiments demonstrate that our method significantly outperforms existing baselines in both accuracy and efficiency, offering a promising solution for real-time, large-scale path inference tasks.