Real-Time, Low-Latency Surveillance Using Entropy-Based Adaptive Buffering and MobileNetV2 on Edge Devices

TL;DR

This paper presents a low-latency, resource-efficient surveillance system using entropy-based adaptive buffering and MobileNetV2, achieving real-time performance on edge devices with high accuracy and robustness.

Contribution

It introduces an entropy-based adaptive buffering algorithm integrated with MobileNetV2 for real-time video analysis on resource-limited edge devices.

Findings

Sub-50ms end-to-end latency on embedded platforms

Over 92% detection accuracy on standard datasets

Robust performance across varying conditions

Abstract

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.