Utrecht Multi-Person Motion ( UMPM ) benchmark

Analyzing human motion, including tracking and pose estimation, is a major topic in computer vision for several decades. Many methods have been and will be developed further in the future. To have a systematic and quantitative evaluation of such methods, ground truth data of the 3D human motion is scientifically required. For scenes limited to only a single person, there exist some publicly available data sets like HumanEva that provide synchronized video sequences together with detailed ground truth 3D data. However, for multiple persons, such a data set currently does not exist. In this report, we present the Utrecht Multi-Person Motion (UMPM) benchmark, which includes synchronized motion capture data and video sequences from multiple viewpoints for multi-person motion including multi-person interaction. The data set is available to the research community via http://www.projects.science.uu.nl/umpm/including documentation and software to promote further research in multi-person articulated human motion analysis. This report gives a detailed literature survey on publicly available data sets, describes the details of the data acquisition, the format of the provided data and the accompanying software.

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[2]  Andrew Zisserman,et al.  Pose search: Retrieving people using their pose , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Bodo Rosenhahn,et al.  Multisensor-fusion for 3D full-body human motion capture , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[4]  Jitendra Malik,et al.  Poselets: Body part detectors trained using 3D human pose annotations , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[5]  Larry S. Davis,et al.  Recognizing actions by shape-motion prototype trees , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[6]  Michael J. Black,et al.  HumanEva: Synchronized Video and Motion Capture Dataset and Baseline Algorithm for Evaluation of Articulated Human Motion , 2010, International Journal of Computer Vision.

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[12]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

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[14]  Cordelia Schmid,et al.  Learning realistic human actions from movies , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Bernt Schiele,et al.  Multi-cue onboard pedestrian detection , 2009, CVPR.

[16]  Tae-Kyun Kim,et al.  Tensor Canonical Correlation Analysis for Action Classification , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

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[18]  B. Caputo,et al.  Recognizing human actions: a local SVM approach , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[19]  Luc Van Gool,et al.  A mobile vision system for robust multi-person tracking , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[20]  Dariu Gavrila,et al.  Monocular Pedestrian Detection: Survey and Experiments , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Zhengyou Zhang,et al.  A Flexible New Technique for Camera Calibration , 2000, IEEE Trans. Pattern Anal. Mach. Intell..

[22]  N. Pettersson,et al.  A new pedestrian dataset for supervised learning , 2008, 2008 IEEE Intelligent Vehicles Symposium.

[23]  Moritz Tenorth,et al.  The TUM Kitchen Data Set of everyday manipulation activities for motion tracking and action recognition , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[24]  Michael J. Black,et al.  HumanEva: Synchronized Video and Motion Capture Dataset for Evaluation of Articulated Human Motion , 2006 .

[25]  Andrew Zisserman,et al.  Long Term Arm and Hand Tracking for Continuous Sign Language TV Broadcasts , 2008, BMVC.

[26]  Alan F. Smeaton,et al.  A Framework for Evaluating Stereo-Based Pedestrian Detection Techniques , 2008, IEEE Transactions on Circuits and Systems for Video Technology.