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

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.

[1]  T Ivan Ciric,et al.  INTELLIGENT OPTIMAL CONTROL OF THERMAL VISION-BASED PERSON-FOLLOWING ROBOT PLATFORM , 2014 .

[2]  Zarko Cojbasic,et al.  Computationally intelligent system for thermal vision people detection and tracking in robotic applications , 2013, 2013 11th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Services (TELSIKS).

[3]  Theodora Varvarigou,et al.  Adaptable neural networks for modeling recursive non-linear systems , 2002, 2002 14th International Conference on Digital Signal Processing Proceedings. DSP 2002 (Cat. No.02TH8628).

[4]  V. Nikoli,et al.  INTELLIGENT CONTROL OF DaNI ROBOT BASED ON ROBOT VISION AND OBJECT RECOGNITION , 2013 .

[5]  Horst-Michael Groß,et al.  An approach to multi-modal human-machine interaction for intelligent service robots , 2003, Robotics Auton. Syst..

[6]  Arie Yeredor,et al.  Yule-Walker Equations Applied to Hessians of the Characteristic Function for Improved AR Estimation , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[7]  Jinsong Leng,et al.  Analysis of Hu's moment invariants on image scaling and rotation , 2010, 2010 2nd International Conference on Computer Engineering and Technology.

[8]  Antonio Fernández Caballero,et al.  Optical flow or image subtraction in human detection from infrared camera on mobile robot , 2010 .

[9]  Hazem M. El-Bakry,et al.  Fast human motion tracking by using high speed neural , 2009 .

[10]  Javier Ramírez De La Pinta,et al.  Integration of service robots in the smart home by means of UPnP: A surveillance robot case study , 2013, Robotics Auton. Syst..

[11]  Grzegorz Cielniak,et al.  Real-time people tracking for mobile robots using thermal vision , 2006, Robotics Auton. Syst..

[12]  Danijela Ristic-Durrant,et al.  Stereo Vision-Based Human Tracking for Robotic Follower , 2013 .

[13]  Xose Manuel Pardo,et al.  Feature analysis for human recognition and discrimination: Application to a person-following behaviour in a mobile robot , 2012, Robotics Auton. Syst..