Real-Time, Low-Latency Surveillance Using Entropy-Based Adaptive Buffering and MobileNetV2 on Edge Devices
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arXiv
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866912436865466368 |
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| author | Sajjanar, Poojashree Chandrashekar Pankaj M |
| author_facet | Sajjanar, Poojashree Chandrashekar Pankaj M |
| contents | This paper describes a high-performance, low-latency video surveillance system designed for resource-constrained environments. We have proposed a formal entropy-based adaptive frame buffering algorithm and integrated that with MobileNetV2 to achieve high throughput with low latency. The system is capable of processing live streams of video with sub-50ms end-to-end inference latency on resource-constrained devices (embedding platforms) such as Raspberry Pi, Amazon, and NVIDIA Jetson Nano. Our method maintains over 92% detection accuracy on standard datasets focused on video surveillance and exhibits robustness to varying lighting, backgrounds, and speeds. A number of comparative and ablation experiments validate the effectiveness of our design. Finally, our architecture is scalable, inexpensive, and compliant with stricter data privacy regulations than common surveillance systems, so that the system could coexist in a smart city or embedded security architecture. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_14833 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Real-Time, Low-Latency Surveillance Using Entropy-Based Adaptive Buffering and MobileNetV2 on Edge Devices Sajjanar, Poojashree Chandrashekar Pankaj M Computer Vision and Pattern Recognition Artificial Intelligence This paper describes a high-performance, low-latency video surveillance system designed for resource-constrained environments. We have proposed a formal entropy-based adaptive frame buffering algorithm and integrated that with MobileNetV2 to achieve high throughput with low latency. The system is capable of processing live streams of video with sub-50ms end-to-end inference latency on resource-constrained devices (embedding platforms) such as Raspberry Pi, Amazon, and NVIDIA Jetson Nano. Our method maintains over 92% detection accuracy on standard datasets focused on video surveillance and exhibits robustness to varying lighting, backgrounds, and speeds. A number of comparative and ablation experiments validate the effectiveness of our design. Finally, our architecture is scalable, inexpensive, and compliant with stricter data privacy regulations than common surveillance systems, so that the system could coexist in a smart city or embedded security architecture. |
| title | Real-Time, Low-Latency Surveillance Using Entropy-Based Adaptive Buffering and MobileNetV2 on Edge Devices |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2506.14833 |