Abstract
Pose Recovery (PR) and Human Behavior Analysis (HBA) have been a main focus of interest from the beginnings of Computer Vision and Machine Learning. PR and HBA were originally addressed by the analysis of still images and image sequences. More recent strategies consisted of Motion Capture technology (MOCAP), based on the synchronization of multiple cameras in controlled environments; and the analysis of depth maps from Time-of-Flight (ToF) technology, based on range image recording from distance sensor measurements. Recently, with the appearance of the multi-modal RGBD information provided by the low cost Kinect\(^{\textsf{TM}}\) sensor (from RGB and Depth, respectively), classical methods for PR and HBA have been redefined, and new strategies have been proposed. In this paper, the recent contributions and future trends of multi-modal RGBD data analysis for PR and HBA are reviewed and discussed.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Jain, H., Subramanian, A.: Real-time upper-body human pose estimation using a depth camera, HP Technical Reports
Rodgers, J., Anguelov, D., Hoi-Cheung, P.: Object pose detection in range scan data. In: CVPR, pp. 2445–2452 (2006)
Ganapathi, V., Plagemann, C., Koller, D., Thrun, S.: Real time motion capture using a single time-of-flight camera. In: CVPR, pp. 755–762 (2010)
Sabata, B., Arman, F., Aggarwal, J.: Segmentation of 3d range images using pyramidal data structures. CVGIP: Image Understanding 57(3), 373–387 (1993)
Primesensor\(\texttrademark\), http://www.primesense.com/?p=514
Shotton, J., Fitzgibbon, A., Cook, M., Sharp, T., Finocchio, M.: Real-time human pose recognition in parts from single depth images (2011)
Dephsense ds311, http://www.softkinetic.com/Solutions/DepthSensecameras.aspx
Openni, http://www.openni.org
Flexible action and articulated skeleton toolkit (faast), http://projects.ict.usc.edu/mxr/faast/
Suma, E., Lange, B., Rizzo, A., Krum, D.M.: FAAST: the flexible action and articulated skeleton toolkit. In: Virtual Reality, Singapore, pp. 245–246 (2011)
Kinect for windows sdk from microsoft research, http://research.microsoft.com/en-us/um/redmond/projects/kinectsdk/
Openkinect (libfreenect), http://openkinect.org/
Code laboratories cl nui platform - kinect driver/sdk, http://codelaboratories.com/nui/
Point cloud library (pcl), http://pointclouds.org/
Rusu, R.B.: Semantic 3D Object Maps for Everyday Manipulation in Human Living Environments. Articial Intelligence (KI-Kuenstliche Intelligenz) (2010)
Lai, K., Bo, L., Ren, X., Fox, D.: Sparse distance learning for object recognition combining rgb and depth information. In: ICRA
Bo, L., Ren, X., Fox, D.: Depth kernel descriptors for object recognition. In: IROS, pp. 821–826 (2011)
Koch, R., Schiller, I., Bartczak, B., Kellner, F., Koser, K.: Mixin3d: 3d mixed reality with tof-camera, pp. 126–141 (2009)
Castaneda, V., Mateus, D., Navab, N.: Slam combining tof and high-resolution cameras. In: WACV, pp. 672–678 (2011)
Gehrig, D., Kuehne, H.: Hmm-based human motion recognition with optical flow data. In: IEEE International Conference on Humanoid Robots, Humanoids 2009 (2009)
Sminchisescu, C., Kanaujia, A., Metaxas, D.: Conditional models for contextual human motion recognition. CVIU 104(2-3), 210–220 (2006)
Zhou, F., la Torre, F.D., Hodgins, J.K.: Aligned cluster analysis for temporal segmentation of human motion. In: IEEE Conference on Automatic Face and Gestures Recognition, FG (2008)
Reyes, M., Dominguez, G., Escalera, S.: Feature weighting in dynamic time warping for gesture recognition in depth data. In: ICCV, Barcelona, Spain (2011)
Hernandez-Vela, A., Zlateva, N., Marinov, A., Reyes, M., Radeva, P., Dimov, D., Escalera, S.: Graph cuts optimization for multi-limb human segmentation in depth maps. In: CVPR (2012)
Hernandez-Vela, A., Reyes, M., Escalera, S., Radeva, P.: Spatio-temporal grabcut human segmentation for face and pose recovery. In: IEEE International Workshop on Analysis and Modeling of Faces and Gestures, CVPR (2010)
Hernandez-Vela, A., Primo, C., Escalera, S.: Automatic user interaction correction via multi-label graph cuts. In: 1st IEEE International Workshop on Human Interaction in Computer Vision HICV, ICCV (2011)
Igual, L., Soliva, J., Hernandez-Vela, A., Escalera, S., Jimenez, X., Vilarroya, O., Radeva, P.: A fully-automatic caudate nucleus segmentation of brain mri: Application in volumetric analysis of pediatric attention-deficit/hyperactivity disorder. In: BioMedical Engineering OnLine (2011)
Liu, Y., Stoll, C., Gall, J., Seidel, H.: Markerless motion capture of interacting characters using multi-view image segmentation. CVPR 14(1), 1249–1256 (2011)
Holt, B., Ong, E.-J., Cooper, H., Bowden, R.: Putting the pieces together: Connected poselets for human pose estimation. In: ICCV (2011)
Pugeault, N., Bowden, R.: Spelling it out: Real-time asl fingerspelling recognition. In: ICCV (2011)
Plagemann, C., Ganapathi, V., Koller, D., Thrun, S.: Real-time identification and localization of body parts from depth images. In: ICCV, pp. 3108–3113 (2011)
Clapes, A., Reyes, M., Escalera, S.: User Identification and Object Recognition in Clutter Scenes Based on RGB-Depth Analysis. In: Perales, F.J., Fisher, R.B., Moeslund, T.B. (eds.) AMDO 2012. LNCS, vol. 7378, pp. 1–11. Springer, Heidelberg (2012)
Charles, J., Everingham, M.: Learning shape models for monocular human pose estimation from the microsoft xbox kinect. In: ICCV, pp. 1202–1208 (2011)
Bo, L., Lai, K., Ren, X., Fox, D.: Object recognition with hierarchical kernel descriptors. In: CVPR (2011)
Aubry, M., Schlickewei, U., Cremers, D.: The wave kernel signature: A quantum mechanical approach to shape analysis. In: ICCV (2011)
Schwarz, L., Mkhitaryan, A., Mateus, D., Navab, N.: Estimating human 3d pose from time-of-flight images based on geodesic distances and optical flow. In: IEEE Conference on Automatic Face and Gesture Recognition, FG (2011)
Ganapathiand, V., Plagemann, C., Koller, D., Thrun, S.: Real time motion capture using a single time-of-flight camera. In: CVPR, pp. 755–762 (2010)
Keskin, C., Racc, F., Kara, Y., Akarun, L.: Real time hand pose estimation using depth sensors. In: ICCV (2011)
Minnen, D., Zafrulla, Z.: Towards robust cross-user hand tracking and shape recognition. In: ICCV, pp. 1235–1241 (2011)
Windheuser, T., Schlickewei, U., Schmidt, F.R.: Geometrically consistent elastic matching of 3d shapes: A linear programming solution. In: ICCV (2011)
Xia, L., Chen, C.-C., Aggarwal, J.K.: Human detection using depth information by kinect department of electrical and computer engineering. PR, 15–22 (2011)
Human pose recovery and behavior analysis group, http://www.maia.ub.es/~sergio/
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Escalera, S. (2012). Human Behavior Analysis from Depth Maps. In: Perales, F.J., Fisher, R.B., Moeslund, T.B. (eds) Articulated Motion and Deformable Objects. AMDO 2012. Lecture Notes in Computer Science, vol 7378. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31567-1_28
Download citation
DOI: https://doi.org/10.1007/978-3-642-31567-1_28
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31566-4
Online ISBN: 978-3-642-31567-1
eBook Packages: Computer ScienceComputer Science (R0)