STAC: Leveraging Spatio-Temporal Data Associations For Efficient Cross-Camera Streaming and Analytics

• URL: http://arxiv.org/abs/2401.15288v2
• Date: Wed, 13 Aug 2025 15:28:59 GMT
• Title: STAC: Leveraging Spatio-Temporal Data Associations For Efficient Cross-Camera Streaming and Analytics
• Authors: Ragini Gupta, Lingzhi Zhao, Jiaxi Li, Volodymyr Vakhniuk, Claudiu Danilov, Josh Eckhardt, Keyshla Bernard, Klara Nahrstedt,
• Abstract summary: In distributed network of cameras, real-time multi-camera video analytics is challenged by high bandwidth demands and redundant visual data.<n>We present STAC, a cross-camera surveillances system that leverages multi-temporal associations for efficient object tracking under constrained network conditions.
• Score: 5.752749052742801
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In IoT based distributed network of cameras, real-time multi-camera video analytics is challenged by high bandwidth demands and redundant visual data, creating a fundamental tension where reducing data saves network overhead but can degrade model performance, and vice versa. We present STAC, a cross-cameras surveillance system that leverages spatio-temporal associations for efficient object tracking under constrained network conditions. STAC integrates multi-resolution feature learning, ensuring robustness under variable networked system level optimizations such as frame filtering, FFmpeg-based compression, and Region-of-Interest (RoI) masking, to eliminate redundant content across distributed video streams while preserving downstream model accuracy for object identification and tracking. Evaluated on NVIDIA's AICity Challenge dataset, STAC achieves a 76\% improvement in tracking accuracy and an 8.6x reduction in inference latency over a standard multi-object multi-camera tracking baseline (using YOLOv4 and DeepSORT). Furthermore, 29\% of redundant frames are filtered, significantly reducing data volume without compromising inference quality.

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