Real-time Human Detection Model for Edge Devices

TL;DR

This paper introduces a lightweight CNN model optimized for real-time human detection on resource-constrained edge devices like Raspberry Pi, achieving faster processing with comparable accuracy to larger models.

Contribution

A novel small-sized CNN model designed specifically for limited-resource edge devices, balancing speed, size, and accuracy in human detection tasks.

Findings

Faster processing time compared to existing models

Smaller model size suitable for edge devices

Maintains comparable accuracy with larger CNNs

Abstract

Building a small-sized fast surveillance system model to fit on limited resource devices is a challenging, yet an important task. Convolutional Neural Networks (CNNs) have replaced traditional feature extraction and machine learning models in detection and classification tasks. Various complex large CNN models are proposed that achieve significant improvement in the accuracy. Lightweight CNN models have been recently introduced for real-time tasks. This paper suggests a CNN-based lightweight model that can fit on a limited edge device such as Raspberry Pi. Our proposed model provides better performance time, smaller size and comparable accuracy with existing method. The model performance is evaluated on multiple benchmark datasets. It is also compared with existing models in terms of size, average processing time, and F-score. Other enhancements for future research are suggested.