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Inter-humanoid robot interaction with emphasis on detection: a comparison study

Published online by Cambridge University Press:  02 February 2017

Taher Abbas Shangari ,
Vida Shams ,
Bita Azari ,
Faraz Shamshirdar ,
Jacky Baltes  and
Soroush Sadeghnejad Open the ORCID record for Soroush Sadeghnejad [Opens in a new window]
Taher Abbas Shangari
Affiliation:
Bio-Inspired System Design Lab, Amirkabir University of Technology (Tehran Polytechnic), No. 424, Hafez Avenue, PO Box 15875-4413, Tehran, Iran e-mail: [email protected]; [email protected]; [email protected]; [email protected]; [email protected]
Vida Shams
Affiliation:
Bio-Inspired System Design Lab, Amirkabir University of Technology (Tehran Polytechnic), No. 424, Hafez Avenue, PO Box 15875-4413, Tehran, Iran e-mail: [email protected]; [email protected]; [email protected]; [email protected]; [email protected]
Bita Azari
Affiliation:
Bio-Inspired System Design Lab, Amirkabir University of Technology (Tehran Polytechnic), No. 424, Hafez Avenue, PO Box 15875-4413, Tehran, Iran e-mail: [email protected]; [email protected]; [email protected]; [email protected]; [email protected]
Faraz Shamshirdar
Affiliation:
Bio-Inspired System Design Lab, Amirkabir University of Technology (Tehran Polytechnic), No. 424, Hafez Avenue, PO Box 15875-4413, Tehran, Iran e-mail: [email protected]; [email protected]; [email protected]; [email protected]; [email protected]
Jacky Baltes
Affiliation:
Autonomous Agents Laboratory, University of Manitoba, Winnipeg, Canada, R3T 2N2 e-mail: [email protected]
Soroush Sadeghnejad
Affiliation:
Bio-Inspired System Design Lab, Amirkabir University of Technology (Tehran Polytechnic), No. 424, Hafez Avenue, PO Box 15875-4413, Tehran, Iran e-mail: [email protected]; [email protected]; [email protected]; [email protected]; [email protected]
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Abstract

Robot Interaction has always been a challenge in collaborative robotics. In tasks comprising Inter-Robot Interaction, robot detection is very often needed. We explore humanoid robots detection because, humanoid robots can be useful in many scenarios, and everything from helping elderly people live in their own homes to responding to disasters. Cameras are chosen because they are reach and cheap sensors, and there are lots of mature two-dimensional (2D) and 3D computer vision libraries which facilitate Image analysis. To tackle humanoid robot detection effectively, we collected a data set of various humanoid robots with different sizes in different environments. Afterward, we tested the well-known cascade classifier in combination with several image descriptors like Histograms of Oriented Gradients (HOG), Local Binary Patterns (LBP), etc. on this data set. Among the feature sets, Haar-like has the highest accuracy, LBP the highest recall, and HOG the highest precision. Considering Inter-Robot Interaction, it is evident that false positives are less troublesome than false negatives, thus LBP is more useful than the others.

Type
Review Article
Information
The Knowledge Engineering Review , Volume 32 , 2017 , e8
Copyright
© Cambridge University Press, 2017 

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Footnotes

†

These authors contributed equally to this work.

References

Alves Jr, A.A., Andrade Filho, L.M., Barbosa, A.F., Bediaga, I., Cernicchiaro, G., Guerrer, G., Lima Jr, H.P., Machado, A.A., Magnin, J., Marujo, F. and De Miranda, J.M., 2008. The LHCb detector at the LHC. Journal of instrumentation, 3(08), p.S08005.Google Scholar
Baltes, J., Tu, K.-Y., Sadeghnejad, S. & Anderson, J. 2016. HuroCup: competition for multi-event humanoid robot athletes. The Knowledge Engineering Review, 1–14.Google Scholar
Bar-Hillel, A., Levi, D., Krupka, E. & Goldberg, C. 2010. Part-based feature synthesis for human detection. In European Conference on Computer Vision, 127–142. Springer Berlin Heidelberg.Google Scholar
Cao, Y. U., Fukunaga, A. S. & Kahng, A. 1997. Cooperative mobile robotics: antecedents and directions. Autonomous Robots 4, 7–27.Google Scholar
Chaimowicz, L., Sugar, T., Kumar, V. & Campos, M. F. 2001. An architecture for tightly coupled multi-robot cooperation. In Proceedings 2001 ICRA, IEEE International Conference on Robotics and Automation, 2001, 2992–2997.Google Scholar
Coates, A. & Ng, A. Y. 2010. Multi-camera object detection for robotics, In IEEE International Conference on Robotics and Automation (ICRA), 2010, 412–419.Google Scholar
Dalal, N. & Triggs, B. 2005. Histograms of oriented gradients for human detection, In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2005, CVPR 2005, 886–893.Google Scholar
Discant, A., Rogozan, A., Rusu, C. & Bensrhair, A. 2007. Sensors for obstacle detection-a survey, In 30th International Spring Seminar on Electronics Technology, 100–105.Google Scholar
Dollár, P., Belongie, S. & Perona, P. 2010. The Fastest Pedestrian Detector in the West. BMVC 2(3).CrossRefGoogle Scholar
Dollar, P., Wojek, C., Schiele, B. & Perona, P. 2012. Pedestrian detection: An evaluation of the state of the art. IEEE transactions on pattern analysis and machine intelligence 34.4, 743–761.CrossRefGoogle Scholar
Dollár, P., Tu, Z., Perona, P. & Belongie, S. 2009. Integral channel features, 91–1.CrossRefGoogle Scholar
Dollár, P., Tu, Z., Tao, H. & Belongie, S. 2007. Feature mining for image classification, In IEEE Conference on Computer Vision and Pattern Recognition, 2007. CVPR'07, 1–8.Google Scholar
Felzenszwalb, P., McAllester, D. & Ramanan, D. 2008. A discriminatively trained, multiscale, deformable part model, In IEEE Conference on Computer Vision and Pattern Recognition, 2008, CVPR 2008, 1–8.Google Scholar
Felzenszwalb, P. F., Girshick, R. B., McAllester, D. & Ramanan, D. 2010. Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence 32, 1627–1645.Google Scholar
Gan, G. & Cheng, J. 2011. Pedestrian detection based on HOG-LBP feature, In Seventh International Conference on Computational Intelligence and Security (CIS), 2011, 1184–1187.Google Scholar
Gerndt, R., Seifert, D., Baltes, J. H., Sadeghnejad, S. & Behnke, S. 2015. Humanoid robots in soccer: robots versus humans in RoboCup 2050. IEEE Robotics & Automation Magazine 22, 147–154.Google Scholar
Guizzo, E. & Ackerman, E. 2012. How rethink robotics built its new Baxter robot worker. http://spectrum.ieee.org/robotics/industrial-robots/rethink-robotics-baxter-robot-factory-worker.Google Scholar
Lin, Z. & Davis, L. S. 2008. A pose-invariant descriptor for human detection and segmentation. In Computer Vision–ECCV 2008 . Springer, 423–436.Google Scholar
Ma, Y., Chen, X. & Chenm, G. 2011. Pedestrian detection and tracking using HOG and oriented-LBP features. Network and Parallel Computing, 176–184.CrossRefGoogle Scholar
Maji, S., Berg, A. C. & Malik, J. 2008. Classification using intersection kernel support vector machines is efficient, In IEEE Conference on Computer Vision and Pattern Recognition, 2008, CVPR 2008, 1–8.Google Scholar
Moeslund, T. B. & Granum, E. 2001. A survey of computer vision-based human motion capture. Computer Vision and Image Understanding 81, 231–268.Google Scholar
Ruiz del Solar, J., Verschae, R., Arenas, M. & Loncomilla, P. 2010. Play Ball!. In IEEE Robotics & Automation Magazine, 17(4), 43–53.Google Scholar
Schneiderman, H. & Kanade, T. 2000. A statistical method for 3D object detection applied to faces and cars, In Proceedings. IEEE Conference on Computer Vision and Pattern Recognition, 2000, 746–751.Google Scholar
Sabzmeydani, P. & Mori, G. 2007. Detecting pedestrians by learning shapelet features, In IEEE Conference on Computer Vision and Pattern Recognition, 2007, CVPR'07, 1–8.Google Scholar
Schwartz, W. R., Kembhavi, A., Harwood, D. & Davis, L. S. 2009. Human detection using partial least squares analysis, In IEEE 12th international conference on Computer Vision, 2009 , 24–31.Google Scholar
Stone, P. 1998. Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer. MIT Press.Google Scholar
Viola, P. & Jones, M. J. 2004. Robust real-time face detection. International Journal of Computer Vision 57, 137–154.Google Scholar
Walk, S., Majer, N., Schindler, K. & Schiele, B. 2010. New features and insights for pedestrian detection, In IEEE conference on Computer Vision and Pattern Recognition (CVPR), 2010 , 1030–1037.Google Scholar
Wang, X., Han, T. X. & Yan, S. 2009. An HOG-LBP human detector with partial occlusion handling, In IEEE 12th International Conference on Computer Vision, 2009, 32–39.Google Scholar
Wojek, C. & Schiele, B. 2008. A Performance Evaluation of Single and Multi-feature People Detection. In Proceedings of the 30th DAGM symposium on Pattern Recognition, Rigoll, G. (ed.). Springer-Verlag, Berlin, Heidelberg, 82–91.Google Scholar
Zaier, R. 2012. The Future of Humanoid Robots – Research and Applications. InTechCorp.Google Scholar