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.
@article{arxiv.2401.15288,
title = {STAC: Leveraging Spatio-Temporal Data Associations For Efficient Cross-Camera Streaming and Analytics},
author = {Ragini Gupta and Lingzhi Zhao and Jiaxi Li and Volodymyr Vakhniuk and Claudiu Danilov and Josh Eckhardt and Keyshla Bernard and Klara Nahrstedt},
journal= {arXiv preprint arXiv:2401.15288},
year = {2025}
}