TRACER: Efficient Object Re-Identification in Networked Cameras through Adaptive Query Processing

• URL: http://arxiv.org/abs/2507.09448v1
• Date: Sun, 13 Jul 2025 02:22:08 GMT
• Title: TRACER: Efficient Object Re-Identification in Networked Cameras through Adaptive Query Processing
• Authors: Pramod Chunduri, Yao Lu, Joy Arulraj,
• Abstract summary: Spatula is the state-of-the-art video database management system (VDBMS) for processing Re-ID queries.<n>It is not suitable for critical video analytics applications that require high recall due to camera history.<n>We present Tracer, a novel VDBMS for efficiently processing Re-ID queries using an adaptive query processing framework.
• Score: 8.955401552705892
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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