People Detection and Tracking with TOF Sensor

2008 ◽  
Author(s):  
Rudolf Tanner ◽  
Martin Studer ◽  
Adriano Zanoli ◽  
Andreas Hartmann
Keyword(s):  
People Detection ◽  
Detection And Tracking

Applications for a people detection and tracking algorithm using a time-of-flight camera

2014 ◽  
Vol 75 (17) ◽  
pp. 10769-10786 ◽  
Author(s):  
Carsten Stahlschmidt ◽  
Alexandros Gavriilidis ◽  
Jörg Velten ◽  
Anton Kummert
Keyword(s):  
Time Of Flight ◽  
Tracking Algorithm ◽  
People Detection ◽  
Detection And Tracking

Multi-Robot Multiple Camera People Detection and Tracking in Automated Warehouses

2021 ◽  
Author(s):  
Michela Zaccaria ◽  
Mikhail Giorgini ◽  
Riccardo Monica ◽  
Jacopo Aleotti
Keyword(s):  
People Detection ◽  
Multiple Camera ◽  
Detection And Tracking ◽  
Automated Warehouses ◽  
Multi Robot

scholarly journals People Detection and Tracking Using LIDAR Sensors

Robotics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 75 ◽  
Author(s):  
Claudia Álvarez-Aparicio ◽  
Ángel Manuel Guerrero-Higueras ◽  
Francisco Javier Rodríguez-Lera ◽  
Jonatan Ginés Clavero ◽  
Francisco Martín Rico ◽  
...  
Keyword(s):  
Service Robots ◽  
Challenging Problem ◽  
Complex Environments ◽  
People Detection ◽  
Test Environment ◽  
Detection And Tracking ◽  
The People ◽  
Imaging Detection ◽  
New Feature ◽  
Robotic Application

The tracking of people is an indispensable capacity in almost any robotic application. A relevant case is the @home robotic competitions, where the service robots have to demonstrate that they possess certain skills that allow them to interact with the environment and the people who occupy it; for example, receiving the people who knock at the door and attending them as appropriate. Many of these skills are based on the ability to detect and track a person. It is a challenging problem, particularly when implemented using low-definition sensors, such as Laser Imaging Detection and Ranging (LIDAR) sensors, in environments where there are several people interacting. This work describes a solution based on a single LIDAR sensor to maintain a continuous identification of a person in time and space. The system described is based on the People Tracker package, aka PeTra, which uses a convolutional neural network to identify person legs in complex environments. A new feature has been included within the system to correlate over time the people location estimates by using a Kalman filter. To validate the solution, a set of experiments have been carried out in a test environment certified by the European Robotic League.

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