scholarly journals Thermal vision based intelligent system for human detection and tracking in mobile robot control system

2016 ◽  
Vol 20 (suppl. 5) ◽  
pp. 1553-1559 ◽  
Author(s):  
Ivan Ciric ◽  
Zarko Cojbasic ◽  
Danijela Ristic-Durrant ◽  
Vlastimir Nikolic ◽  
Milica Ciric ◽  
...  

This paper presents the results of the authors in thermal vision based mobile robot control. The most important segment of the high level control loop of mobile robot platform is an intelligent real-time algorithm for human detection and tracking. Temperature variations across same objects, air flow with different temperature gradients, reflections, person overlap while crossing each other, and many other non-linearities, uncertainty and noise, put challenges in thermal image processing and therefore the need of computationally intelligent algorithms for obtaining the efficient performance from human motion tracking system. The main goal was to enable mobile robot platform or any technical system to recognize the person in indoor environment, localize it and track it with accuracy high enough to allow adequate human-machine interaction. The developed computationally intelligent algorithms enables robust and reliable human detection and tracking based on neural network classifier and autoregressive neural network for time series prediction. Intelligent algorithm used for thermal image segmentation gives accurate inputs for classification.

2014 ◽  
pp. 64-68
Author(s):  
Oleh Adamiv ◽  
Vasyl Koval ◽  
Iryna Turchenko

This paper describes the experimental results of neural networks application for mobile robot control on predetermined trajectory of the road. There is considered the formation process of training sets for neural network, their structure and simulating features. Researches have showed robust mobile robot movement on different parts of the road.


2003 ◽  
Vol 36 (17) ◽  
pp. 491-496
Author(s):  
Vinícius Menezes de Oliveira ◽  
Walter Fetter Lages ◽  
Edson Roberto de Pieri

1997 ◽  
Vol 08 (03) ◽  
pp. 279-293 ◽  
Author(s):  
Doo-Hyun Choi ◽  
Se-Young Oh

The feasibility of using neural networks for camera localization and mobile robot control is investigated here. This approach has the advantages of eliminating the laborious and error-prone process of imaging system modeling and calibration procedures. Basically, two different approaches of using neural networks are introduced of which one is a hybrid approach combining neural networks and the pinhole-based analytic solution while the other is purely neural network based. These techniques have been tested and compared through both simulation and real-time experiments and are shown to yield more precise localization than analytic approaches. Furthermore, this neural localization method is also shown to be directly applicable to the navigation control of an experimental mobile robot along the hallway purely guided by a dark wall strip. It also facilitates multi-sensor fusion through the use of multiple sensors of different types for control due to the network's capability of learning without models.


1996 ◽  
Vol 4 (2) ◽  
pp. 193-199 ◽  
Author(s):  
K. Watanabe ◽  
Jun Tang ◽  
M. Nakamura ◽  
S. Koga ◽  
T. Fukuda

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