International Scientific Journal


In this paper the supervisory control of the Person-Following Robot Platform is presented. The main part of the high level control loop of mobile robot platform is a real-time robust algorithm for human detection and tracking. The main goal was to enable mobile robot platform to recognize the person in indoor environment, and to localize it with accuracy high enough to allow adequate human-robot interaction. The developed computationally intelligent control algorithm enables robust and reliable human tracking by mobile robot platform. The core of the recognition methods proposed is genetic optimization of threshold segmentation and classification of detected regions of interests in every frame acquired by thermal vision camera. The support vector machine classifier determines whether the segmented object is human or not based on features extracted from the processed thermal image independently from current light conditions and in situations where no skin color is visible. Variation in temperature across same objects, air flow with different temperature gradients, person overlap while crossing each other and reflections, put challenges in thermal imaging and will have to be handled intelligently in order to obtain the efficient performance from motion tracking system. [Projekat Ministarstva nauke Republike Srbije, br. TR35005]
PAPER REVISED: 2014-04-17
PAPER ACCEPTED: 2014-05-06
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THERMAL SCIENCE YEAR 2014, VOLUME 18, ISSUE Issue 3, PAGES [957 - 966]
  1. Alvarez-Santos, et al., Feature Analysis for Human Recognition and Discrimination: Application to a Person- Following Behaviour in a Mobile Robot, Robotics and Autonomous Systems, 60 (2012), 8, pp. 1021-1036
  2. Bohme, H.-J., et al., An Approach to Multi-Modal Human-Machine Interaction for Intelligent Service Robots, Robotics and Autonomous Systems, 44 (2003), 1, pp. 83-96
  3. Ćirić, I., et al., Computationally Intelligent System for Thermal Vision People Detection and Tracking in Robotic Applications, Proceedings, 11th International Conference on Telecommunications in Modern Satellite, Cable and Broadcasting Services - TELSIKS 2013, Nis, Serbia, 2013, pp. 587-590
  4. Borja, R., et al., Integration of Service Robots in the Smart Home by Means of UPnP: A Surveillance Robot Case Study, Robotics and Autonomous Systems, 61 (2013), 2, pp. 153-160
  5. Fernandez-Caballero, et al., Optical Flow or Image Subtraction in Human Detection from Infrared Camera on Mobile Robot, Robotics and Autonomous Systems, 58 (2010), 12, pp.1273-1281
  6. Ćirič I., et al., Intelligent Control of DaNI Robot Based on Robot Vision and Object Recognition, Facta Universitatis - Series: Automatic Control and Robotics, 11 (2012), 2, pp. 129-140
  7. Treptow, A., et al., Real-Time People Tracking for Mobile Robots Using Thermal Vision, Robotics and Autonomous Systems, 54 (2006), 9, pp. 729-739
  8. Ristic-Durrant, D., et al., Robust Stereo-Vision Based 3D Object Reconstruction for the Assistive Robot FRIEND, Advances in Electrical and Computer Engineering, 11 (2011), 4, pp. 15-22
  9. Tao, W., et al., Object Segmentation Using ant Colony Optimization Algorithm and Fuzzy Entropy, Pattern Recognition Letters, 28 (2007), 7, pp. 788-796
  10. Guofeng, J., et al., Image Segmentation of Thermal Waving Inspection Based on Particle Swarm Optimization Fuzzy Clustering Algorithm, Measurement Science Review, 12 (2012), 6, pp. 296-301
  11. Senthil Kumar, K., et al., Visual and Thermal Image Fusion for UAV Based Target Tracking, (Ed. C. M. Ionescu) in: MATLAB - A Ubiquitous Tool for the Practical Engineer, In Tech, 2011, ISBN 978-953-307-907-3, pp. 307-326
  12. St-Laurent, L., et al., Thermal Imaging for Enhanced Foreground-Background Segmentation, Proceedings, 8th International Conference on Quantitative Infrared Thermography; Padova, Italy, 2006, pp. 065:1-10
  13. Ćirič, I., et al., Intelligent Control System for Thermal Vision-Based Person-Following Robot Platform, Proceedings, 16th Symposium on Thermal Science and Engineering SIMTERM, Sokobanja, Serbia, 2013, pp. 640-648
  14. Dudić, S. P., et al., Leakage Quantification of Compressed Air on Pipes Using Thermovision. Thermal Science, 16 (2012), Suppl. 2, pp. 555-565
  15. Čojbašić, . M., et al.,Computationally Intelligent Modeling and Control of fluidized Bed Combustion Process, Thermal Science, 15 (2011), 2, pp. 321-338
  16. Huang, Z., Leng, J., Analysis of Hu's Moment invariants on Image Scaling and Rotation, Proceedings, 2nd International Conference on Computer Engineering and Technology (ICCET), Chengdu, China, 2010, vol. 7, pp. V7-476-V7-480
  17. Cortes, C., Vapnik, V., Support-Vector Networks, Machine Learning, 20 (1995), 3, pp. 273-297
  18. Ravi Kumar., N, et al., Towards Artificial Intelligence Based Diesel Engine Performance Control under Varying Operating Conditions Using Support Vector Regression, Thermal Science, 17 (2013), 1, pp. 167-178
  19. Osuna, E., et al., Training Support Vector Machines: an Application to Face Detection, Proceedings, Computer Vision and Pattern Recognition, 1997 IEEE Computer Society Conference on San Juan, Puerto Rico, pp. 130-136
  20. Stojanović, M. B. et al., A Methodology for Training Set Instance Selection Using Mutual Information in time Series Prediction, Neurocomputing, Available online April 8, 2014, ISSN 0925-2312

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