STAC: Leveraging Spatio-Temporal Data Associations For Efficient Cross-Camera Streaming and Analytics
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arXiv
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| Autores principales: | , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866912536091164672 |
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| author | Gupta, Ragini Zhao, Lingzhi Li, Jiaxi Vakhniuk, Volodymyr Danilov, Claudiu Eckhardt, Josh Bernard, Keyshla Nahrstedt, Klara |
| author_facet | Gupta, Ragini Zhao, Lingzhi Li, Jiaxi Vakhniuk, Volodymyr Danilov, Claudiu Eckhardt, Josh Bernard, Keyshla Nahrstedt, Klara |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2401_15288 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | STAC: Leveraging Spatio-Temporal Data Associations For Efficient Cross-Camera Streaming and Analytics Gupta, Ragini Zhao, Lingzhi Li, Jiaxi Vakhniuk, Volodymyr Danilov, Claudiu Eckhardt, Josh Bernard, Keyshla Nahrstedt, Klara Computer Vision and Pattern Recognition Multimedia Networking and Internet Architecture I.4.2; I.4.0; C.2.2; C.2.0 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. |
| title | STAC: Leveraging Spatio-Temporal Data Associations For Efficient Cross-Camera Streaming and Analytics |
| topic | Computer Vision and Pattern Recognition Multimedia Networking and Internet Architecture I.4.2; I.4.0; C.2.2; C.2.0 |
| url | https://arxiv.org/abs/2401.15288 |