用熵值自适应缓冲+MobileNetV2，在边缘设备上实现亚50ms低延迟实时监控。
Real-Time, Low-Latency Surveillance Using Entropy-Based Adaptive Buffering and MobileNetV2 on Edge Devices
- 基于视频帧熵值动态调整缓冲策略，减少冗余计算。
- 在树莓派等设备上实现<50ms端到端延迟，检测准确率超92%。
- 适合隐私敏感场景，可部署于智慧城市与嵌入式安防系统。
本文提出一种高性能、低延迟的视频监控系统，专为资源受限环境设计。通过引入基于熵的自适应帧缓冲算法，并结合MobileNetV2模型，实现在树莓派、亚马逊嵌入式平台及NVIDIA Jetson Nano等设备上处理实时视频流时，端到端推理延迟低于50ms。系统在标准视频监控数据集上保持超过92%的检测准确率，对光照变化、背景干扰和目标速度波动均具有鲁棒性。多组对比与消融实验验证了设计的有效性。该架构具备可扩展性、低成本优势，且符合更严格的隐私保护要求，适用于智慧城市或嵌入式安全体系中的共存部署。
原文摘要 · Abstract (English)
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.
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