A Data Fusion Perspective on Human Motion Analysis Including Multiple Camera Applications

Human motion analysis methods have received increasing attention during the last two decades. In parallel, data fusion technologies have emerged as a powerful tool for the estimation of properties of objects in the real world. This papers presents a view of human motion analysis from the viewpoint of data fusion. JDL process model and Dasarathy’s input-output hierarchy are employed to categorize the works in the area. A survey of the literature in human motion analysis from multiple cameras is included. Future research directions in the area are identified after this review.

[1]  Ioannis Pitas,et al.  The i3DPost Multi-View and 3D Human Action/Interaction Database , 2009, 2009 Conference for Visual Media Production.

[2]  Alexandros Iosifidis,et al.  Multi-view human movement recognition based on fuzzy distances and linear discriminant analysis , 2012, Comput. Vis. Image Underst..

[3]  Mubarak Shah,et al.  Learning 4D action feature models for arbitrary view action recognition , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[4]  Sidney S. Fels,et al.  A Multi-Camera Surveillance System that Estimates Quality-of-View Measurement , 2007, 2007 IEEE International Conference on Image Processing.

[5]  Ioannis Pitas,et al.  3D Human Action Recognition for Multi-view Camera Systems , 2011, 2011 International Conference on 3D Imaging, Modeling, Processing, Visualization and Transmission.

[6]  James Llinas,et al.  Handbook of Multisensor Data Fusion : Theory and Practice, Second Edition , 2008 .

[7]  Gang Qian,et al.  View-invariant full-body gesture recognition via multilinear analysis of voxel data , 2009, 2009 Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC).

[8]  Thomas B. Moeslund,et al.  A Local 3-D Motion Descriptor for Multi-View Human Action Recognition from 4-D Spatio-Temporal Interest Points , 2012, IEEE Journal of Selected Topics in Signal Processing.

[9]  J.K. Aggarwal,et al.  Human activity analysis , 2011, ACM Comput. Surv..

[10]  Miguel A. Patricio,et al.  A probabilistic, discriminative and distributed system for the recognition of human actions from multiple views , 2012, Neurocomputing.

[11]  Miguel A. Patricio,et al.  Multicamera Action Recognition with Canonical Correlation Analysis and Discriminative Sequence Classification , 2011, IWINAC.

[12]  James Llinas,et al.  Handbook of Multisensor Data Fusion , 2001 .

[13]  Miguel A. Patricio,et al.  Distributed Data and Information Fusion in Visual Sensor Networks , 2012 .

[14]  Henry A. Kautz,et al.  Generalized Plan Recognition , 1986, AAAI.

[15]  Avinash C. Kak,et al.  Distributed and lightweight multi-camera human activity classification , 2009, 2009 Third ACM/IEEE International Conference on Distributed Smart Cameras (ICDSC).

[16]  Vinodkrishnan Kulathumani,et al.  Real-time multi-view human action recognition using a wireless camera network , 2011, 2011 Fifth ACM/IEEE International Conference on Distributed Smart Cameras.

[17]  Ling Shao,et al.  Multi-view action recognition using local similarity random forests and sensor fusion , 2013, Pattern Recognit. Lett..

[18]  Belur V. Dasarathy,et al.  Sensor fusion potential exploitation-innovative architectures and illustrative applications , 1997, Proc. IEEE.

[19]  Moataz M. Abdelwahab,et al.  Multi-view human action recognition system employing 2DPCA , 2011, 2011 IEEE Workshop on Applications of Computer Vision (WACV).

[20]  Mario Cannataro,et al.  Protein-to-protein interactions: Technologies, databases, and algorithms , 2010, CSUR.

[21]  Sridha Sridharan,et al.  Multi-view human pose estimation using modified five-point skeleton model , 2008 .

[22]  Lihi Zelnik-Manor,et al.  Viewpoint Selection for Human Actions , 2012, International Journal of Computer Vision.

[23]  Ying Wang,et al.  Multi-view Gymnastic Activity Recognition with Fused HMM , 2007, ACCV.

[24]  José Manuel Ferrández,et al.  Foundations on Natural and Artificial Computation , 2011, Lecture Notes in Computer Science.

[25]  Alan N. Steinberg,et al.  Revisions to the JDL data fusion model , 1999, Defense, Security, and Sensing.

[26]  Pinar Duygulu Sahin,et al.  A new pose-based representation for recognizing actions from multiple cameras , 2011, Comput. Vis. Image Underst..

[27]  Hamid K. Aghajan,et al.  On efficient use of multi-view data for activity recognition , 2010, ICDSC '10.

[28]  Chen Wu,et al.  Multiview activity recognition in smart homes with spatio-temporal features , 2010, ICDSC '10.

[29]  Miguel A. Patricio,et al.  Context-based scene recognition from visual data in smart homes: an Information Fusion approach , 2012, Personal and Ubiquitous Computing.

[30]  B. S. Manjunath,et al.  Probabilistic subspace-based learning of shape dynamics modes for multi-view action recognition , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[31]  D. J. Barrett,et al.  Model-Data Fusion , 2003 .