Tracking persons using a network of RGBD cameras

A computer vision system that employs an RGBD camera network to track multiple humans is presented. The acquired views are used to volumetrically and photometrically reconstruct and track the humans robustly and in real time. Given the frequent and accurate monitoring of humans in space and time, their locations and walk-through trajectory can be robustly tracked in real-time.

[1]  L. Davis,et al.  M2Tracker: A Multi-View Approach to Segmenting and Tracking People in a Cluttered Scene , 2003, International Journal of Computer Vision.

[2]  Kaj Grønbæk,et al.  Interactive spatial multimedia for communication of art in the physical museum space , 2008, ACM Multimedia.

[3]  Henry Fuchs,et al.  Reducing interference between multiple structured light depth sensors using motion , 2012, 2012 IEEE Virtual Reality Workshops (VRW).

[4]  Ramakant Nevatia,et al.  CLEAR'07 Evaluation of USC Human Tracking System for Surveillance Videos , 2007, CLEAR.

[5]  Antonis A. Argyros,et al.  Multicamera tracking of multiple humans based on colored visual hulls , 2013, 2013 IEEE 18th Conference on Emerging Technologies & Factory Automation (ETFA).

[6]  Pascal Fua,et al.  Multicamera People Tracking with a Probabilistic Occupancy Map , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[7]  Rainer Stiefelhagen,et al.  Real-Time GPU-Based Voxel Carving with Systematic Occlusion Handling , 2009, DAGM-Symposium.

[8]  Kai Oliver Arras,et al.  People tracking in RGB-D data with on-line boosted target models , 2011, 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[9]  Marcus A. Magnor,et al.  Markerless Motion Capture using multiple Color-Depth Sensors , 2011, VMV.

[10]  Larry S. Davis,et al.  M2Tracker: A Multi-view Approach to Segmenting and Tracking People in a Cluttered Scene Using Region-Based Stereo , 2002, ECCV.

[11]  Antonis A. Argyros,et al.  Multicamera human detection and tracking supporting natural interaction with large-scale displays , 2012, Machine Vision and Applications.

[12]  Manolis I. A. Lourakis,et al.  Real-Time Tracking of Multiple Skin-Colored Objects with a Possibly Moving Camera , 2004, ECCV.

[13]  J. Edmonds Paths, Trees, and Flowers , 1965, Canadian Journal of Mathematics.

[14]  Nassir Navab,et al.  Efficient visual hull computation for real-time 3D reconstruction using CUDA , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[15]  Peter H. N. de With,et al.  Employing a RGB-D sensor for real-time tracking of humans across multiple re-entries in a smart environment , 2012, IEEE Transactions on Consumer Electronics.

[16]  David Kim,et al.  Shake'n'sense: reducing interference for overlapping structured light depth cameras , 2012, CHI.

[17]  Dariu Gavrila,et al.  Multi-person Tracking with Overlapping Cameras in Complex, Dynamic Environments , 2009, BMVC.

[18]  Emilio J. Almazan,et al.  Tracking People across Multiple Non-overlapping RGB-D Sensors , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition Workshops.

[19]  Andrew W. Fitzgibbon,et al.  Real-time human pose recognition in parts from single depth images , 2011, CVPR 2011.

[20]  Trevor Darrell,et al.  Integrated Person Tracking Using Stereo, Color, and Pattern Detection , 2000, International Journal of Computer Vision.

[21]  Volkan Cevher,et al.  Compressed sensing for multi-view tracking and 3-D voxel reconstruction , 2008, 2008 15th IEEE International Conference on Image Processing.

[22]  Mubarak Shah,et al.  A Multiview Approach to Tracking People in Crowded Scenes Using a Planar Homography Constraint , 2006, ECCV.

[23]  Larry S. Davis,et al.  UMD_VDT, an Integration of Detection and Tracking Methods for Multiple Human Tracking , 2007, CLEAR.

[24]  Flavia Sparacino Scenographies of the past and museums of the future: from the wunderkammer to body-driven interactive narrative spaces , 2004, MULTIMEDIA '04.