Detecting subtle human-object interactions using kinect

We present a method to identify human-object interactions involved in complex, fine-grained activities. Our approach benefits from recent improvements in range sensor technology and body trackers to detect and classify important events in a depth video. Combining global motion information with local...

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Autor principal: Ubalde, S.
Otros Autores: Liu, Z., Mejail, M., Hancock E., Bayro-Corrochano E.
Formato: Acta de conferencia Capítulo de libro
Lenguaje:Inglés
Publicado: Springer Verlag 2014
Acceso en línea:Registro en Scopus
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100 1 |a Ubalde, S. 
245 1 0 |a Detecting subtle human-object interactions using kinect 
260 |b Springer Verlag  |c 2014 
270 1 0 |m Ubalde, S.; Departamento de Computación, Universidad de Buenos AiresArgentina 
506 |2 openaire  |e Política editorial 
504 |a Oreifej, O., Liu, Z., Hon4d: Histogram of oriented 4D normals for activity recognition from depth sequences (2013) CVPR 2013, pp. 716-723 
504 |a Wang, J., Liu, Z., Wu, Y., Yuan, J., Mining actionlet ensemble for action recognition with depth cameras (2012) CVPR 2012, pp. 1290-1297 
504 |a Vieira, A., Nascimento, E., Oliveira, G., Liu, Z., Campos, M., Stop: Space-time occupancy patterns for 3D action recognition from depth map sequences (2012) CIARP 2012. LNCS, 7441, pp. 252-259. , Alvarez, L., Mejail, M., Gomez, L., Jacobo, J. (eds.),,. Springer, Heidelberg 
504 |a Li, W., Zhang, Z., Liu, Z., Action recognition based on a bag of 3D points (2010) CVPR4HB 2010, pp. 9-14 
504 |a Sung, J., Ponce, C., Selman, B., Saxena, A., Human activity detection from RGBD images (2011) AAAI workshop on Pattern, , Activity and Intent Recognition, PAIR 
504 |a Mehrotra, S., Zhang, Z., Cai, Q., Zhang, C., Chou, P.A., Low-complexity, nearlossless coding of depth maps from kinect-like depth cameras (2011) MMSP, pp. 1-6. , IEEE 
504 |a Camplani, M., Salgado, L., Efficient spatio-temporal hole filling strategy for Kinect depth maps (2012) Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series, 8290. , (February, Society of Photo-Optical Instrumentation Engineers (SPIE) Conference Series 
504 |a Gupta, A., Davis, L., Objects in action: An approach for combining action understanding and object perception (2007) CVPR 2007, pp. 1-8 
504 |a Gupta, A., Kembhavi, A., Davis, L., Observing human-object interactions: Using spatial and functional compatibility for recognition (2009) PAMI, 31, pp. 1775-1789 
504 |a Packer, B., Saenko, K., Koller, D., A combined pose, object, and feature model for action understanding (2012) 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1378-1385. , (June 
504 |a Datasets, , http://www-2.dc.uba.ar/grupinv/imagenes/subalde/ 
504 |a Shotton, J., Sharp, T., Kipman, A., Fitzgibbon, A., Finocchio, M., Blake, A., Cook, M., Moore, R., Real-time human pose recognition in parts from single depth images (2013) CACM, 56, pp. 116-124. , (January 
504 |a Perona, P., Malik, J., Scale space and edge detection using anisotropic diffusion (1987) CVWS 1987, pp. 16-22 
504 |a Rao, C., Yilmaz, A., Shah, M., View-invariant representation and recognition of actions (2002) IJCV, 50, pp. 203-226. , (NovemberA4 - Chilean Association for Pattern Recognition (AChiRP); CINVESTAV, Campus Guadalajara; Cuban Association for Pattern Recognition (ACRP); INTEL Education; International Association for Pattern Recognition (IAPR); Mexican Association for Computer Vision; Neurocomputing and Robotics (MACVNR); Portuguese Association for Pattern Recognition (APRP); Spanish Association for Pattern Recogntion and Image Analysis (AERFAI); Special Interest Group of the Brazilian Computer Society (SIGPR-SBC) 
520 3 |a We present a method to identify human-object interactions involved in complex, fine-grained activities. Our approach benefits from recent improvements in range sensor technology and body trackers to detect and classify important events in a depth video. Combining global motion information with local video analysis, our method is able to recognize the time instants of a video at which a person picks up or puts down an object. We introduce three novel datasets for evaluation and perform extensive experiments with promising results. © Springer International Publishing Switzerland 2014.  |l eng 
593 |a Departamento de Computación, Universidad de Buenos Aires, Buenos Aires, Argentina 
593 |a Microsoft Research, Redmond, United States 
690 1 0 |a DEPTH SENSOR 
690 1 0 |a HUMAN-OBJECT INTERACTION 
690 1 0 |a TRAJECTORY ANALYSIS 
690 1 0 |a COMPUTER VISION 
690 1 0 |a DEPTH SENSORS 
690 1 0 |a DEPTH VIDEOS 
690 1 0 |a FINE GRAINED 
690 1 0 |a GLOBAL MOTION 
690 1 0 |a HUMAN-OBJECT INTERACTION 
690 1 0 |a RANGE SENSORS 
690 1 0 |a TRAJECTORY ANALYSIS 
690 1 0 |a VIDEO ANALYSIS 
690 1 0 |a PATTERN RECOGNITION 
700 1 |a Liu, Z. 
700 1 |a Mejail, M. 
700 1 |a Hancock E. 
700 1 |a Bayro-Corrochano E. 
711 2 |d 2 November 2014 through 5 November 2014  |g Código de la conferencia: 109889 
773 0 |d Springer Verlag, 2014  |g v. 8827  |h pp. 770-777  |p Lect. Notes Comput. Sci.  |n Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)  |x 03029743  |w (AR-BaUEN)CENRE-983  |z 9783319125671  |t 19th Iberoamerican Congress on Pattern Recognition, CIARP 2014 
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