Aggarwal, J., Ryoo, M., 2011. Human activity analysis: A review. ACM
690 Comput. Surv. 43, 16:1{16:43.
691 Ahad, M.A.R., Tan, J.K., Kim, H., Ishikawa, S., 2012. Motion history image:
692 its variants and applications. Mach. Vision Appl. 23, 255{281.
693 Allen, J., 1983. Maintaining knowledge about temporal intervals. Commun.
694 ACM 26, 832{843.
695 Anguelov, D., Taskarf, B., Chatalbashev, V., Koller, D., Gupta, D., Heitz,
696 G., Ng, A., 2005. Discriminative learning of Markov random �elds for
697 segmentation of 3D scan data, in: IEEE Computer Society Conference on
698 Computer Vision and Pattern Recognition, CVPR 2005, pp. 169{176.
699 Batlle, J., Mouaddib, E., Salvi, J., 1998. Recent progress in coded structured
700 light as a technique to solve the correspondence problem: a survey. Pattern
701 Recognition 31, 963{ 982.
702 Benko, H., Wilson, A.D., 2009. Depthtouch: Using depth-sensing camera to
703 enable freehand interactions on and above the interactive surface. IEEE
704 Workshop on Tabletops and Interactive Surfaces .
705 Van den Bergh, M., Van Gool, L., 2011. Combining RGB and ToF cameras
706 for real-time 3D hand gesture interaction, in: 2011 IEEE Workshop on
707 Applications of Computer Vision (WACV), pp. 66 {72.
708 Besl, P., McKay, H., 1992. A method for registration of 3-D shapes. IEEE
709 Transactions on Pattern Analysis and Machine Intelligence 14, 239{256.
Bobick, A., Davis, J., 2001. The recognition of human movement using
711 temporal templates. IEEE Transactions on Pattern Analysis and Machine
712 Inelligence 23, 257{ 267.
713 Breuer, P., Eckes, C., Mu�ller, S., 2007. Hand gesture recognition with a
714 novel ir time-of-
ight range camera: a pilot study, in: Proceedings of
715 the 3rd international conference on Computer vision/computer graphics
716 collaboration techniques, Springer-Verlag, Berlin, Heidelberg. pp. 247{260.
717 Charles, J., Everingham, M., 2011. Learning shape models for monocular
718 human pose estimation from the Microsoft Xbox Kinect, in: 2011 IEEE
719 International Conference on Computer Vision Workshops (ICCVW), pp.
720 1202{1208.
721 Chen, C.S., Hung, Y.P., Chiang, C.C., Wu, J.L., 1997. Range data acqui722
sition using color structured lighting and stereo vision. Image and Vision
723 Computing 15, 445{ 456.
724 Chen, L., Wei, H., Ferryman, J., 2011. Recognition of everyday domestic
725 activities using a depth sensor, in: BMVC 2011 student workshop, pp.
726 27{37.
727 Demirdjian, D., Ko, T., Darrell, T., 2003. Constraining human body track728
ing, in: 2003 Proceedings. Ninth IEEE International Conference on Com729
puter Vision, pp. 1071{1078 vol.2.
730 Dolla�r, P., Rabaud, V., Cottrell, G., Belongie, S., 2005. Behavior recognition
731 via sparse spatio-temporal features. Visual Surveillance and Performance
732 Evaluation of Tracking and Surveillance (VS-PETS) 0, 65{72.
Ganapathi, V., Plagemann, C., Koller, D., Thrun, S., 2010. Real time motion
734 capture using a single time-of-
ight camera, in: 2010 IEEE Conference on
735 Computer Vision and Pattern Recognition (CVPR), pp. 755{762.
736 Girshick, R., Shotton, J., Kohli, P., Criminisi, A., Fitzgibbon, A., 2011.
737 E�cient regression of general-activity human poses from depth images, in:
738 Computer Vision (ICCV), 2011 IEEE International Conference on, pp. 415
739 {422.
740 Grammalidis, N., Goussis, G., Troufakos, G., Strintzis, M., 2001. 3-D hu741
man body tracking from depth images using analysis by synthesis, in: Pro742
ceedings. 2001 International Conference on Image Processing, pp. 185{188
743 vol.2.
744 Grest, D., Woetzel, J., Koch, R., 2005. Nonlinear body pose estimation from
745 depth images, in: Proceedings of the 27th DAGM conference on Pattern
746 Recognition, Springer-Verlag, Berlin, Heidelberg. pp. 285{292.
747 Guomundsson, S., Larsen, R., Aanaes, H., Pardas, M., Casas, J., 2008. TOF
748 imaging in smart room environments towards improved people tracking,
749 in: 2008. IEEE Computer Society Conference on Computer Vision and
750 Pattern Recognition Workshops (CVPRW), pp. 1{6.
751 Hartley, R.I., Zisserman, A., 2004. Multiple View Geometry in Computer
752 Vision. Cambridge University Press, ISBN: 0521540518. second edition.
753 Holt, B., Ong, E.J., Cooper, H., Bowden, R., 2011. Putting the pieces to754
gether: Connected poselets for human pose estimation, in: 2011 IEEE
International Conference on Computer Vision Workshops (ICCV Work756
shops), pp. 1196{1201.
757 Holte, M.B., Moeslund, T.B., 2007. Gesture recognition using a range cam758
era. Technical Report , 1{5.
759 Hu, G., Stockman, G., 1989. 3-D surface solution using structured light
760 and constraint propagation. IEEE Transactions on Pattern Analysis and
761 Machine Intelligence 11, 390{402.
762 Iddan, G.J., Yahav, G., 2001. 3D imaging in the studio. IN: SPIE 4298,
763 48{55.
764 Jansen, B., Temmermans, F., Deklerck, R., 2007. 3D human pose recognition
765 for home monitoring of elderly, in: Engineering in Medicine and Biology
766 Society, EMBS 2007. 29th Annual International Conference of the IEEE,
767 pp. 4049{4051.
768 Ji, X., Liu, H., 2010. Advances in view-invariant human motion analysis: A
769 review. Systems, Man, and Cybernetics, Part C: Applications and Reviews,
770 IEEE Transactions on 40, 13 {24.
771 Johansson, G., 1973. Visual perception of biological motion and a model for
772 its analysis. Attention, Perception, and Psychophysics 14, 201{211.
773 Kalogerakis, E., Hertzmann, A., Singh, K., 2010. Learning 3D mesh segmen774
tation and labeling, in: ACM SIGGRAPH 2010, ACM, New York, NY,
775 USA. pp. 102:1{102:12.
International Conference on Computer Vision Workshops (ICCV Work756
shops), pp. 1196{1201.
757 Holte, M.B., Moeslund, T.B., 2007. Gesture recognition using a range cam758
era. Technical Report , 1{5.
759 Hu, G., Stockman, G., 1989. 3-D surface solution using structured light
760 and constraint propagation. IEEE Transactions on Pattern Analysis and
761 Machine Intelligence 11, 390{402.
762 Iddan, G.J., Yahav, G., 2001. 3D imaging in the studio. IN: SPIE 4298,
763 48{55.
764 Jansen, B., Temmermans, F., Deklerck, R., 2007. 3D human pose recognition
765 for home monitoring of elderly, in: Engineering in Medicine and Biology
766 Society, EMBS 2007. 29th Annual International Conference of the IEEE,
767 pp. 4049{4051.
768 Ji, X., Liu, H., 2010. Advances in view-invariant human motion analysis: A
769 review. Systems, Man, and Cybernetics, Part C: Applications and Reviews,
770 IEEE Transactions on 40, 13 {24.
771 Johansson, G., 1973. Visual perception of biological motion and a model for
772 its analysis. Attention, Perception, and Psychophysics 14, 201{211.
773 Kalogerakis, E., Hertzmann, A., Singh, K., 2010. Learning 3D mesh segmen774
tation and labeling, in: ACM SIGGRAPH 2010, ACM, New York, NY,
775 USA. pp. 102:1{102:12.
Knoop, S., Vacek, S., Dillmann, R., 2009. Fusion of 2D and 3D sensor data
777 for articulated body tracking. Robotics Autonomous Systems 57, 321{329.
778 Kolb, A., Barth, E., Koch, R., 2008. Tof-sensors: New dimensions for realism
779 and interactivity. IEEE Computer Society Conference On Computer Vision
780 and Pattern Recognition Workshops (CVPRW) , 1518{1523.
781 Kollorz, E., Penne, J., Hornegger, J., Barke, A., 2008. Gesture recognition
782 with a time-of-
ight camera. International Journal of Intelligent Systems
783 Technologies and Applications 5, 334.
784 Kurakin, A., Zhang, Z., Liu, Z., 2012. A real time system for dynamic hand
785 gesture recognition with a depth sensor, in: Signal Processing Conference
786 (EUSIPCO), 2012 Proceedings of the 20th European, pp. 1975 {1979.
787 Laptev, I., Marszalek, M., Schmid, C., Rozenfeld, B., 2008. Learning realistic
788 human actions from movies, in: IEEE Conference on Computer Vision and
789 Pattern Recognition, CVPR 2008., pp. 1{8.
790 Li, W., Zhang, Z., Liu, Z., 2010. Action recognition based on a bag of 3D
791 points, in: 2010 IEEE Computer Society Conference on Computer Vision
792 and Pattern Recognition Workshops (CVPRW), pp. 9{14.
793 Lui, Y.M., 2012. A least squares regression framework on manifolds and
794 its application to gesture recognition, in: 2012 IEEE Computer Soci795
ety Conference on Computer Vision and Pattern Recognition Workshops
796 (CVPRW), pp. 13 {18.
Malgireddy, M., Inwogu, I., Govindaraju, V., 2012. A temporal bayesian
798 model for classifying, detecting and localizing activities in video sequences,
in: 2012 IEEE Computer Society Conference on Computer Vision and
800 Pattern Recognition Workshops (CVPRW), pp. 43 {48.
801 Marszalek, M., Laptev, I., Schmid, C., 2009. Actions in context, in: IEEE
802 Conference on Computer Vision and Pattern Recognition, CVPR 2009.,
803 pp. 2929{2936.
804 Moeslund, T., Hilton, A., Kru�ger, V., 2006. A survey of advances in vision805
based human motion capture and analysis. Computer Vision and Image
806 Understanding 104, 90{126.
807 Ni, B., Wang, G., Moulin, P., 2011. RGBD-HuDaAct: A color-depth video
808 database for human daily activity recognition, in: 2011 IEEE International
809 Conference on Computer Vision Workshops (ICCV Workshops), pp. 1147{
810 1153.
811 Oggier, T., Bu�ttgen, B., Lustenberger, F., Becker, G., Ru�egg, B., Hodac,
812 A., 2005. Swissranger SR3000 and �rst experiences based on mniaturized
813 3D-ToF cameras., in: In Proc. of the First Range Imaging Research Day
814 at ETH Zurich.
815 Pellegrini, S., Iocchi, L., 2008. Human posture tracking and classi�cation
816 through stereo vision and 3D model matching. J. Image Video Process.
817 2008, 7:1{7:12.
Phillips, P., Flynn, P., Scruggs, T., Bowyer, K., Chang, J., Ho�man, K.,
819 Marques, J., Min, J., Worek, W., 2005. Overview of the face recogni820
tion grand challenge, in: Computer Vision and Pattern Recognition, 2005.
821 CVPR 2005. IEEE Computer Society Conference on, pp. 947 { 954 vol. 1.
Plagemann, C., Ganapathi, V., Koller, D., Thrun, S., 2010. Real-time iden823
ti�cation and localization of body parts from depth images, in: IEEE
824 International Conference on Robotics and Automation (ICRA).
825 Poppe, R., 2010. A survey on vision-based human action recognition. Image
826 Vision Comput. 28, 976{990.
827 Reyes, M., Dominguez, G., Escalera, S., 2011. Featureweighting in dynamic
828 timewarping for gesture recognition in depth data, in: 2011 IEEE Inter829
national Conference on Computer Vision Workshops (ICCVW), pp. 1182{
830 1188.
831 Rodriguez, M., Ahmed, J., Shah, M., 2008. Action MACH a spatio-temporal
832 maximum average correlation height �lter for action recognition, in: IEEE
833 Conference on Computer Vision and Pattern Recognition, CVPR 2008.,
834 pp. 1{8.
835 Roh, M.C., Shin, H.K., Lee, S.W., 2010. View-independent human action
836 recognition with volume motion template on single stereo camera. Pattern
837 Recogn. Lett. 31, 639{647.
838 Rusu, R., Cousins, S., 2011. 3D is here: Point Cloud Library (PCL), in: 2011
839 IEEE International Conference on Robotics and Automation (ICRA), pp.
840 1{4.
Scharstein, D., Szeliski, R., 2003. High-accuracy stereo depth maps using
842 structured light, in: Proceedings. 2003 IEEE Computer Society Conference
843 on Computer Vision and Pattern Recognition (CVPR), pp. I{195{ I{202
844 vol.1.
Schuldt, C., Laptev, I., Caputo, B., 2004. Recognizing human actions: a
846 local svm approach, in: Proceedings of the 17th International Conference
847 on Pattern Recognition, ICPR 2004., pp. 32{ 36 Vol.3.
848 Schwarz, L.A., Mateus, D., Castaneda, V., Nava, N., 2010. Manifold learning
849 for tof-based human body tracking and activity recognition, in: British
850 Machine Vision Conference (BMVC).
851 Schwarz, L.A., Mateus, D., Navab, N., 2012a. Recognizing multiple human
852 activities and tracking full-body pose in unconstrained environments. Pat853
tern Recognition 45, 11{ 23.
854 Schwarz, L.A., Mkhitaryan, A., Mateus, D., Navab, N., 2011. Estimating
855 human 3D pose from Time-of-Flight images based on geodesic distances
856 and optical
ow, in: FG, pp. 700{706.
857 Schwarz, L.A., Mkhitaryan, A., Mateus, D., Navab, N., 2012b. Human skele858
ton tracking from depth data using geodesic distances and optical
ow.
859 Image and Vision Computing 30, 217{ 226.
860 Sempena, S., Maulidevi, N., Aryan, P., 2011. Human action recognition using
861 Dynamic Time Warping, in: 2011 International Conference on Electrical
862 Engineering and Informatics (ICEEI), pp. 1 {5.
Shirai, Y., Suwa, M., 1971. Recognition of polyhedrons with a range �nder,
864 in: Proceedings of the 2nd international joint conference on Arti�cial in865
telligence, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
866 pp. 80{87.
Shotton, J., Girshick, R., Fitzgibbon, A., Sharp, T., Cook, M., Finocchio, M.,
868 Moore, R., Kohli, P., Criminisi, A., Kipman, A., Blake, A., 2012. E�cient
869 human pose estimation from single depth images. Pattern Analysis and
870 Machine Intelligence, IEEE Transactions on PP, 1.
871 Siddiqui, M., Medioni, G., 2010. Human pose estimation from a single view
872 point, real-time range sensor, in: 2010 IEEE Computer Society Conference
873 on Computer Vision and Pattern Recognition Workshops (CVPRW), pp.
874 1{8.
875 Singh, S., Velastin, S.A., Ragheb, H., 2010. Muhavi: A multicamera human
876 action video dataset for the evaluation of action recognition methods, in:
877 Proceedings of the 2010 7th IEEE International Conference on Advanced
878 Video and Signal Based Surveillance, IEEE Computer Society, Washing879
ton, DC, USA. pp. 48{55.
880 Sung, J., Ponce, C., Selman, B., Saxena, A., 2012. Unstructured Human Ac881
tivity Detection from RGBD Images. 2012 IEEE International Conference
882 on Robotics and Automation .
883 Suryanarayan, P., Subramanian, A., Mandalapu, D., 2010. Dynamic hand
884 pose recognition using depth data, in: Proceedings of the 2010 20th In885
ternational Conference on Pattern Recognition, IEEE Computer Society Washington, DC, USA. pp. 3105{3108.
887 Trucco, E., Verri, A., 1998. Introductory Techniques for 3-D Computer Vi888
sion. Prentice Hall PTR, Upper Saddle River, NJ, USA.
Vuylsteke, P., Oosterlinck, A., 1990. Range image acquisition with a single
890 binary-encoded light pattern. IEEE Transactions on Pattern Analysis and
891 Machine Intelligence 12, 148{164.
892 Wang, J., Liu, Z., Chorowski, J., Chen, Z., Wu, Y., 2012a. Robust 3D
893 action recognition with random occupancy patterns, in: Computer Vision
894 { ECCV 2012, pp. 872{885.
895 Wang, J., Liu, Z., Wu, Y., Yuan, J., 2012b. Mining actionlet ensemble
896 for action recognition with depth cameras, in: 2012 IEEE Conference on
897 Computer Vision and Pattern Recognition (CVPR), pp. 1290 {1297.
898 Weinland, D., Ronfard, R., Boyer, E., 2010. A survey of vision-based methods
899 for action representation, segmentation and recognition. Compouter Vision
900 and Image Understanding 115, 224{241.
901 Werghi, N., Xiao, Y., 2002. Recognition of human body posture from a cloud
902 of 3D data points using wavelet transform coe�cients, in: Proceedings of
903 the Fifth IEEE International Conference on Automatic Face and Gesture
904 Recognition, pp. 70{75.
905 Wigdor, D., Wixon, D., 2011. Brave NUI World: Designing Natural User
906 Interfaces for Touch and Gesture. Morgan Kaufmann.
907 Will, P.M., Pennington, K.S., 1971. Grid coding: a preprocessing technique
908 for robot and machine vision, in: Proceedings of the 2nd international
909 joint conference on Arti�cial intelligence, Morgan Kaufmann Publishers
910 Inc., San Francisco, CA, USA. pp. 66{70.
Wolf, C., Mille, J., Lombardi, L., Celiktutan, O., Jiu, M., Baccouche, M.,
912 Dellandrea, E., Bichot, C.E., Garcia, C., Sankur, B., 2012. The LIRIS
913 Human activities dataset and the ICPR 2012 human activities recogni914
tion and localization competition. Technical Report. RR-LIRIS-2012-004,
915 LIRIS Laboratory.
916 Wu, D., Zhu, F., Shao, L., 2012. One shot learning gesture recognition from
917 RGBD images, in: 2012 IEEE Computer Society Conference on Computer
918 Vision and Pattern Recognition Workshops (CVPRW), pp. 7 {12.
919 Xia, L., Chen, C.C., Aggarwal, J., 2012. View invariant human action recog920
nition using histograms of 3d joints, in: Computer Vision and Pattern
921 Recognition Workshops (CVPRW), 2012 IEEE Computer Society Confer922
ence on, pp. 20 {27.
923 Xu, Z., Schwarte, R., Heinol, H., Buxbaum, B., Ringbeck, T., Nachrichten924
verarbeitung, I., Gmbh, S.t., Stra�e, K., 1998. Smart pixel photonic mixer
925 device ( PMD ) New system concept of a 3D-imaging camera-on-a-chip.
926 Proc Int Conf on Mechatron Machine Vision , 259{264.
927 Zhang, H., Parker, L.E., 2011. 4-dimensional local spatio-temporal features
928 for human activity recognition, in: 2011 IEEE/RSJ International Confer929
ence on Intelligent Robots and Systems (IROS), pp. 2044{2049.
Zhu, Y., Dariush, B., Fujimura, K., 2008. Controlled human pose estimation
931 from depth image streams, in: IEEE Computer Society Conference on
932 Computer Vision and Pattern Recognition Workshops, CVPRW 2008, pp.
933 1{8.
Zhu, Y., Fujimura, K., 2007. Constrained optimization for human pose esti935
mation from depth sequences, in: Proceedings of the 8th Asian conference
936 on Computer vision - Volume Part I, Springer-Verlag, Berlin, Heidelberg.
937 pp. 408{418.
938 Zhu, Y., Fujimura, K., 2010. A bayesian framework for human body pose
939 tracking from depth image sequences. Sensors 10, 5280{5293.