# Real-time pedestrian recognition on low computational resources

arXiv: https://arxiv.org/abs/2309.01353 · HTML: https://mlsanity.com/paper/2309.01353/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 43 references that are in the map.
- First posted on arXiv 4 Sep 2023.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [You Only Look Once: Unified, Real-Time Object Detection](https://mlsanity.com/paper/1506.02640/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Fast R-CNN](https://mlsanity.com/paper/1504.08083/) (2015)
- [Rich feature hierarchies for accurate object detection and semantic segmentation](https://mlsanity.com/paper/1311.2524/) (2013)
- [Time Efficiency in Optimization with a Bayesian-Evolutionary Algorithm](https://mlsanity.com/paper/2005.04166/) (2020)
- [Vision-Based Human Pose Estimation via Deep Learning: A Survey](https://mlsanity.com/paper/2308.13872/) (2023)
- [Learning Directed Locomotion in Modular Robots with Evolvable Morphologies](https://mlsanity.com/paper/2001.07804/) (2020)
- [Learning Locomotion Skills in Evolvable Robots](https://mlsanity.com/paper/2010.09531/) (2020)
- [A Semantic Web Technology Index](https://mlsanity.com/paper/2201.07034/) (2022)
- [Class Binarization to NeuroEvolution for Multiclass Classification](https://mlsanity.com/paper/2308.13876/) (2023)
- [LCDet: Low-Complexity Fully-Convolutional Neural Networks for Object Detection in Embedded Systems](https://mlsanity.com/paper/1705.05922/) (2017)
- [REDBEE: A Visual-Inertial Drone System for Real-Time Moving Object Detection](https://mlsanity.com/paper/1712.09162/) (2017)

## Abstract (arXiv)

Pedestrian recognition has successfully been applied to security, autonomous cars, Aerial photographs. For most applications, pedestrian recognition on small mobile devices is important. However, the limitations of the computing hardware make this a challenging task. In this work, we investigate real-time pedestrian recognition on small physical-size computers with low computational resources for faster speed. This paper presents three methods that work on the small physical size CPUs system. First, we improved the Local Binary Pattern (LBP) features and Adaboost classifier. Second, we optimized the Histogram of Oriented Gradients (HOG) and Support Vector Machine. Third, We implemented fast Convolutional Neural Networks (CNNs). The results demonstrate that the three methods achieved real-time pedestrian recognition at an accuracy of more than 95% and a speed of more than 5 fps on a small physical size computational platform with a 1.8 GHz Intel i5 CPU. Our methods can be easily applied to small mobile devices with high compatibility and generality.