Query-Aware Path Inference from Spatial Videos

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.