# Real-Time Pedestrian Detection on IoT Edge Devices: A Lightweight Deep Learning Approach

arXiv: https://arxiv.org/abs/2409.15740 · HTML: https://mlsanity.com/paper/2409.15740/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 52 references that are in the map.
- First posted on arXiv 24 Sep 2024.

## Builds on (most cited first, 17 of 17)

- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [CenterNet: Keypoint Triplets for Object Detection](https://mlsanity.com/paper/1904.08189/) (2019)
- [CrowdHuman: A Benchmark for Detecting Human in a Crowd](https://mlsanity.com/paper/1805.00123/) (2018)
- [Speed/accuracy trade-offs for modern convolutional object detectors](https://mlsanity.com/paper/1611.10012/) (2016)
- [PCANet: A Simple Deep Learning Baseline for Image Classification?](https://mlsanity.com/paper/1404.3606/) (2014)
- [Is Faster R-CNN Doing Well for Pedestrian Detection?](https://mlsanity.com/paper/1607.07032/) (2016)
- [ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation](https://mlsanity.com/paper/1511.07053/) (2015)
- [Ten Years of Pedestrian Detection, What Have We Learned?](https://mlsanity.com/paper/1411.4304/) (2014)
- [Pushing the Limits of Deep CNNs for Pedestrian Detection](https://mlsanity.com/paper/1603.04525/) (2016)
- [Hybrid Channel Based Pedestrian Detection](https://mlsanity.com/paper/1912.12431/) (2019)

## Abstract (arXiv)

Artificial intelligence (AI) has become integral to our everyday lives. Computer vision has advanced to the point where it can play the safety critical role of detecting pedestrians at road intersections in intelligent transportation systems and alert vehicular traffic as to potential collisions. Centralized computing analyzes camera feeds and generates alerts for nearby vehicles. However, real-time applications face challenges such as latency, limited data transfer speeds, and the risk of life loss. Edge servers offer a potential solution for real-time applications, providing localized computing and storage resources and lower response times. Unfortunately, edge servers have limited processing power. Lightweight deep learning (DL) techniques enable edge servers to utilize compressed deep neural network (DNN) models. The research explores implementing a lightweight DL model on Artificial Intelligence of Things (AIoT) edge devices. An optimized You Only Look Once (YOLO) based DL model is deployed for real-time pedestrian detection, with detection events transmitted to the edge server using the Message Queuing Telemetry Transport (MQTT) protocol. The simulation results demonstrate that the optimized YOLO model can achieve real-time pedestrian detection, with a fast inference speed of 147 milliseconds, a frame rate of 2.3 frames per second, and an accuracy of 78%, representing significant improvements over baseline models.