A multi-pedestrian detection and counting system using fusion of stereo camera and laser scanner

2010 ◽  
Author(s):  
Bo Ling ◽  
Spandan Tiwari ◽  
Zhuang Li ◽  
David R. P. Gibson
Keyword(s):  
Pedestrian Detection ◽  
Laser Scanner ◽  
Counting System ◽  
Stereo Camera ◽  
Detection And Counting

scholarly journals PEDESTRIAN DETECTION BY LASER SCANNING AND DEPTH IMAGERY

2016 ◽  
Vol XLI-B3 ◽  
pp. 465-468
Author(s):  
A. Barsi ◽  
T. Lovas ◽  
B. Molnar ◽  
A. Somogyi ◽  
Z. Igazvolgyi
Keyword(s):  
Laser Scanning ◽  
Pedestrian Detection ◽  
Laser Scanner ◽  
Point Clouds ◽  
Shopping Mall ◽  
Complex Flow ◽  
Video Footage ◽  
Flow Parameters ◽  
Dense Point ◽  
Commercial Applications

Pedestrian flow is much less regulated and controlled compared to vehicle traffic. Estimating flow parameters would support many safety, security or commercial applications. Current paper discusses a method that enables acquiring information on pedestrian movements without disturbing and changing their motion. Profile laser scanner and depth camera have been applied to capture the geometry of the moving people as time series. Procedures have been developed to derive complex flow parameters, such as count, volume, walking direction and velocity from laser scanned point clouds. Since no images are captured from the faces of pedestrians, no privacy issues raised. The paper includes accuracy analysis of the estimated parameters based on video footage as reference. Due to the dense point clouds, detailed geometry analysis has been conducted to obtain the height and shoulder width of pedestrians and to detect whether luggage has been carried or not. The derived parameters support safety (e.g. detecting critical pedestrian density in mass events), security (e.g. detecting prohibited baggage in endangered areas) and commercial applications (e.g. counting pedestrians at all entrances/exits of a shopping mall).

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.

Pedestrian Detection and Tracking in an Urban Environment Using a Multilayer Laser Scanner

2010 ◽  
Vol 11 (3) ◽  
pp. 579-588 ◽  
Author(s):  
Samuel Gidel ◽  
Paul Checchin ◽  
Christophe Blanc ◽  
Thierry Chateau ◽  
Laurent Trassoudaine
Keyword(s):  
Urban Environment ◽  
Pedestrian Detection ◽  
Laser Scanner ◽  
Detection And Tracking

Prediction of stopping distance for autonomous emergency braking using stereo camera pedestrian detection

2021 ◽  
Author(s):  
Sivaramakrishnan Rajendar ◽  
Dhivya Rathinasamy ◽  
R. Pavithra ◽  
Vishnu Kumar Kaliappan ◽  
S. Gnanamurthy
Keyword(s):  
Pedestrian Detection ◽  
Stereo Camera ◽  
Emergency Braking ◽  
Stopping Distance

Multiple pedestrian detection using IR LED stereo camera

2007 ◽  
Author(s):  
Bo Ling ◽  
Michael I. Zeifman ◽  
David R.P. Gibson
Keyword(s):  
Pedestrian Detection ◽  
Stereo Camera

NAVIGATION OF AN AUTONOMOUS SEWER INSPECTION ROBOT BASED ON STEREO CAMERA IMAGES AND LASER SCANNER DATA

2007 ◽  
Vol 16 (04) ◽  
pp. 611-625 ◽  
Author(s):  
ALIREZA AHRARY ◽  
LI TIAN ◽  
SEI-ICHIRO KAMATA ◽  
MASUMI ISHIKAWA
Keyword(s):  
Laser Scanner ◽  
Distance Estimation ◽  
Test Field ◽  
Scanner Data ◽  
Sewer Pipe ◽  
Stereo Camera ◽  
Pipe System ◽  
Inspection Robot ◽  
Accurate Localization ◽  
Sewer Inspection

Sewer environment is composed of cylindrical pipes, in which only a few landmarks such as manholes, inlets and pipe joints are available for localization. This paper presents a method for navigation of an autonomous sewer inspection robot in a sewer pipe system based on detection of landmarks. In this method, location of an autonomous sewer inspection robot in the sewer pipe system is estimated from stereo camera images. The laser scanner data are also used to ensure accurate localization of the landmarks and reduce the error in distance estimation by image processing. The method is implemented and evaluated in a sewer pipe test field using a prototype robot, demonstrating its effectiveness.

Sign in / Sign up

Export Citation Format

Share Document