2025/07/13 by Pramod Chunduri, Yao Lu, Chunduri, Pramod +3
Computer Science · Engineering · #Advanced Image and Video Retrieval Techniques #Computer Vision and Pattern Recognition (cs.CV) #Databases (cs.DB) #FOS: Computer and information sciences #Infrared Target Detection Methodologies #Video Surveillance and Tracking Methods
paper · pdf · doi:10.48550/arxiv.2507.09448
openalex publication_date 2025/07/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Efficiently re-identifying and tracking objects across a network of cameras is crucial for applications like traffic surveillance. Spatula is the state-of-the-art video database management system (VDBMS) for processing Re-ID queries. However, it suffers from two limitations. Its spatio-temporal filtering scheme has limited accuracy on large camera networks due to localized camera history. It is not suitable for critical video analytics applications that require high recall due to a lack of support for adaptive query processing. In this paper, we present Tracer, a novel VDBMS for efficiently processing Re-ID queries using an adaptive query processing framework. Tracer selects the optimal camera to process at each time step by training a recurrent network to model long-term historical correlations. To accelerate queries under a high recall constraint, Tracer incorporates a probabilistic adaptive search model that processes camera feeds in incremental search windows and dynamically updates the sampling probabilities using an exploration-exploitation strategy. To address the paucity of benchmarks for the Re-ID task due to privacy concerns, we present a novel synthetic benchmark for generating multi-camera Re-ID datasets based on real-world traffic distribution. Our evaluation shows that Tracer outperforms the state-of-the-art cross-camera analytics system by 3.9x on average across diverse datasets.