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

arXiv: https://arxiv.org/abs/1805.00330 · HTML: https://mlsanity.com/paper/1805.00330/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2018.

## Built on by (most cited first, 8 of 8)

- [Real-World Graph Convolution Networks (RW-GCNs) for Action Recognition in Smart Video Surveillance](https://mlsanity.com/paper/2201.05739/) (2022)
- [Illumination and Temperature-Aware Multispectral Networks for Edge-Computing-Enabled Pedestrian Detection](https://mlsanity.com/paper/2112.05053/) (2021)
- [Real-time Human Detection Model for Edge Devices](https://mlsanity.com/paper/2111.10653/) (2021)
- [AutoLungDx: A Hybrid Deep Learning Approach for Early Lung Cancer Diagnosis Using 3D Res-U-Net, YOLOv5, and Vision Transformers](https://mlsanity.com/paper/2305.00046/) (2023)
- [MODIPHY: Multimodal Obscured Detection for IoT using PHantom Convolution-Enabled Faster YOLO](https://mlsanity.com/paper/2402.07894/) (2024)
- [Achieving Pareto Optimality using Efficient Parameter Reduction for DNNs in Resource-Constrained Edge Environment](https://mlsanity.com/paper/2403.10569/) (2024)
- [GuideMe: A Mobile Application based on Global Positioning System and Object Recognition Towards a Smart Tourist Guide](https://mlsanity.com/paper/2105.13426/) (2021)
- [Developing a Resource-Constraint EdgeAI model for Surface Defect Detection](https://mlsanity.com/paper/2401.05355/) (2023)

## Abstract (arXiv)

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.