Real-Time Human Detection as an Edge Service Enabled by a Lightweight CNN

TL;DR

This paper introduces a lightweight CNN designed for real-time human detection on edge devices, enabling fast, on-site surveillance analysis with low computational cost, demonstrated on a Raspberry Pi 3.

Contribution

A novel lightweight CNN based on depthwise separable convolution and SSD, optimized for human detection on resource-limited edge devices.

Findings

Effective real-time detection on Raspberry Pi 3

Low computational workload suitable for edge deployment

Validated with real-world surveillance videos

Abstract

Edge computing allows more computing tasks to take place on the decentralized nodes at the edge of networks. Today many delay sensitive, mission-critical applications can leverage these edge devices to reduce the time delay or even to enable real time, online decision making thanks to their onsite presence. Human objects detection, behavior recognition and prediction in smart surveillance fall into that category, where a transition of a huge volume of video streaming data can take valuable time and place heavy pressure on communication networks. It is widely recognized that video processing and object detection are computing intensive and too expensive to be handled by resource limited edge devices. Inspired by the depthwise separable convolution and Single Shot Multi-Box Detector (SSD), a lightweight Convolutional Neural Network (LCNN) is introduced in this paper. By narrowing down the classifier's searching space to focus on human objects in surveillance video frames, the proposed LCNN algorithm is able to detect pedestrians with an affordable computation workload to an edge device. A prototype has been implemented on an edge node (Raspberry PI 3) using openCV libraries, and satisfactory performance is achieved using real world surveillance video streams. The experimental study has validated the design of LCNN and shown it is a promising approach to computing intensive applications at the edge.