{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,5]],"date-time":"2026-01-05T18:29:09Z","timestamp":1767637749972,"version":"3.48.0"},"reference-count":31,"publisher":"Maximum Academic Press","license":[{"start":{"date-parts":[[2017,2,2]],"date-time":"2017-02-02T00:00:00Z","timestamp":1485993600000},"content-version":"unspecified","delay-in-days":32,"URL":"https:\/\/www.cambridge.org\/core\/terms"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["The Knowledge Engineering Review"],"published-print":{"date-parts":[[2017]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>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.<\/jats:p>","DOI":"10.1017\/s0269888916000321","type":"journal-article","created":{"date-parts":[[2017,2,2]],"date-time":"2017-02-02T03:10:07Z","timestamp":1486005007000},"source":"Crossref","is-referenced-by-count":4,"title":["Inter-humanoid robot interaction with emphasis on detection: a comparison study"],"prefix":"10.48130","volume":"32","author":[{"given":"Taher Abbas","family":"Shangari","sequence":"first","affiliation":[]},{"given":"Vida","family":"Shams","sequence":"additional","affiliation":[]},{"given":"Bita","family":"Azari","sequence":"additional","affiliation":[]},{"given":"Faraz","family":"Shamshirdar","sequence":"additional","affiliation":[]},{"given":"Jacky","family":"Baltes","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8286-7756","authenticated-orcid":false,"given":"Soroush","family":"Sadeghnejad","sequence":"additional","affiliation":[]}],"member":"27968","published-online":{"date-parts":[[2017,2,2]]},"reference":[{"key":"S0269888916000321_ref11","doi-asserted-by":"crossref","unstructured":"Doll\u00e1r P. , Tu Z. , Perona P. & Belongie S. 2009. 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Detecting pedestrians by learning shapelet features, In IEEE Conference on Computer Vision and Pattern Recognition, 2007, CVPR'07, 1\u20138."},{"key":"S0269888916000321_ref22","unstructured":"Ruiz del Solar J. , Verschae R. , Arenas M. & Loncomilla P. 2010. Play Ball!. In IEEE Robotics & Automation Magazine, 17(4), 43\u201353."},{"key":"S0269888916000321_ref2","first-page":"1","article-title":"HuroCup: competition for multi-event humanoid robot athletes","author":"Baltes","year":"2016","journal-title":"The Knowledge Engineering Review"},{"key":"S0269888916000321_ref1","unstructured":"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."},{"key":"S0269888916000321_ref21","doi-asserted-by":"publisher","DOI":"10.1006\/cviu.2000.0897"},{"key":"S0269888916000321_ref15","unstructured":"Gan G. & Cheng J. 2011. Pedestrian detection based on HOG-LBP feature, In Seventh International Conference on Computational Intelligence and Security (CIS), 2011, 1184\u20131187."},{"key":"S0269888916000321_ref17","doi-asserted-by":"crossref","unstructured":"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.","DOI":"10.1109\/MSPEC.2012.6309254"},{"key":"S0269888916000321_ref23","unstructured":"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\u2013751."},{"key":"S0269888916000321_ref8","unstructured":"Discant A. , Rogozan A. , Rusu C. & Bensrhair A. 2007. Sensors for obstacle detection-a survey, In 30th International Spring Seminar on Electronics Technology, 100\u2013105."},{"key":"S0269888916000321_ref25","unstructured":"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\u201331."},{"key":"S0269888916000321_ref16","doi-asserted-by":"publisher","DOI":"10.1109\/MRA.2015.2448811"},{"key":"S0269888916000321_ref30","first-page":"82","volume-title":"Proceedings of the 30th DAGM symposium on Pattern Recognition","author":"Wojek","year":"2008"},{"key":"S0269888916000321_ref4","doi-asserted-by":"publisher","DOI":"10.1023\/A:1008855018923"},{"key":"S0269888916000321_ref7","unstructured":"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\u2013893."},{"key":"S0269888916000321_ref13","unstructured":"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\u20138."},{"key":"S0269888916000321_ref29","unstructured":"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\u201339."},{"key":"S0269888916000321_ref31","doi-asserted-by":"publisher","DOI":"10.5772\/1407"},{"key":"S0269888916000321_ref6","unstructured":"Coates A. & Ng A. Y. 2010. Multi-camera object detection for robotics, In IEEE International Conference on Robotics and Automation (ICRA), 2010, 412\u2013419."},{"key":"S0269888916000321_ref19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-24403-2_15"},{"key":"S0269888916000321_ref10","doi-asserted-by":"publisher","DOI":"10.1109\/TPAMI.2011.155"},{"key":"S0269888916000321_ref5","unstructured":"Chaimowicz L. , Sugar T. , Kumar V. & Campos M. F. 2001. An architecture for tightly coupled multi-robot cooperation. 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