scholarly journals Assuring the Safety of Machine Learning for Pedestrian Detection at Crossings

2020 ◽  
pp. 197-212
Author(s):  
Lydia Gauerhof ◽  
Richard Hawkins ◽  
Chiara Picardi ◽  
Colin Paterson ◽  
Yuki Hagiwara ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pedestrian Detection

scholarly journals Robust pedestrian detection for driver assistance systems using machine learning

2020 ◽  
Vol 83 (2/3/4) ◽  
pp. 140
Author(s):  
Sabrine Hamdi ◽  
Souhir Sghaier ◽  
Hassene Faiedh ◽  
Chokri Souani
Keyword(s):  
Machine Learning ◽  
Pedestrian Detection ◽  
Driver Assistance ◽  
Driver Assistance Systems ◽  
Assistance Systems

scholarly journals Approach to Define the Reliability of Safety-Related Machine Learning Based Functions in Highly Automated Driving

2021 ◽  
Author(s):  
Pengyu Si ◽  
Ossmane Krini ◽  
Nadine Müller ◽  
Aymen Ouertani
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Pedestrian Detection ◽  
Quality Criteria ◽  
Automated Driving ◽  
Safety Requirements ◽  
Functional Reliability ◽  
Current Standards ◽  
Highly Automated Driving

Current standards cannot cover the safety requirements of machine learning based functions used in highly automated driving. Because of the opacity of neural networks, some self-driving functions cannot be developed following the V-model. These functions require the expansion of the standards. This paper focuses on this gap and defines functional reliability for such functions to help the future standards control the quality of machine learning based functions. As an example, reliability functions for pedestrian detection are built. Since the quality criteria in computer vision do not consider safety, new approaches for expression and evaluation of this reliability are designed.

Two-stage Pedestrian Detection Based on Multiple Features and Machine Learning

2012 ◽  
Vol 38 (3) ◽  
pp. 375-381 ◽  
Author(s):  
Yan-Wen CHONG ◽  
Hu-Lin KUANG ◽  
Qing-Quan LI
Keyword(s):  
Machine Learning ◽  
Pedestrian Detection ◽  
Two Stage ◽  
Multiple Features

scholarly journals A Machine Learning Approach to Pedestrian Detection for Autonomous Vehicles Using High-Definition 3D Range Data

Sensors ◽  
2016 ◽  
Vol 17 (12) ◽  
pp. 18 ◽  
Author(s):  
Pedro Navarro ◽  
Carlos Fernández ◽  
Raúl Borraz ◽  
Diego Alonso
Keyword(s):  
Machine Learning ◽  
Autonomous Vehicles ◽  
Pedestrian Detection ◽  
Learning Approach ◽  
Range Data ◽  
High Definition ◽  
Machine Learning Approach ◽  
3D Range Data

scholarly journals Analysis of pedestrian activity before and during COVID-19 lockdown, using webcam time-lapse from Cracow and machine learning

PeerJ ◽  
2020 ◽  
Vol 8 ◽  
pp. e10132
Author(s):  
Robert Szczepanek
Keyword(s):  
Machine Learning ◽  
Public Space ◽  
Learning Algorithm ◽  
Pedestrian Detection ◽  
Computation Time ◽  
Movement Control ◽  
Time Lapse ◽  
Efficient Detection ◽  
Detection And Counting ◽  
Standard Application

At the turn of February and March 2020, COVID-19 pandemic reached Europe. Many countries, including Poland imposed lockdown as a method of securing social distance between potentially infected. Stay-at-home orders and movement control within public space not only affected the touristm industry, but also the everyday life of the inhabitants. The hourly time-lapse from four HD webcams in Cracow (Poland) are used in this study to estimate how pedestrian activity changed during COVID-19 lockdown. The collected data covers the period from 9 June 2016 to 19 April 2020 and comes from various urban zones. One zone is tourist, one is residential and two are mixed. In the first stage of the analysis, a state-of-the-art machine learning algorithm (YOLOv3) is used to detect people. Additionally, a non-standard application of the YOLO method is proposed, oriented to the images from HD webcams. This approach (YOLOtiled) is less prone to pedestrian detection errors with the only drawback being the longer computation time. Splitting the HD image into smaller tiles increases the number of detected pedestrians by over 50%. In the second stage, the analysis of pedestrian activity before and during the COVID-19 lockdown is conducted for hourly, daily and weekly averages. Depending on the type of urban zone, the number of pedestrians decreased from 33% in residential zones to 85% in tourist zones located in the Old Town. The presented method allows for more efficient detection and counting of pedestrians from HD time-lapse webcam images compared to SSD, YOLOv3 and Faster R-CNN. The result of the research is a published database with the detected number of pedestrians from the four-year observation period for four locations in Cracow.

Machine Learning Based Bounding Box Regression for Improved Pedestrian Detection

2019 ◽  
Author(s):  
Tugce Toprak ◽  
Serkan Gunel ◽  
Burak Belenlioglu ◽  
Burak Aydin ◽  
E. Yesim Zoral ◽  
...  
Keyword(s):  
Machine Learning ◽  
Pedestrian Detection ◽  
Bounding Box
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