Pictorial Structures Model based human interaction recognition

In this paper, we propose an interaction recognition system using a single camera in the outdoor scene. The proposed system consists of three parts: human detection, pose estimation, and interaction recognition. We build a new human pose model by integrating Pictorial Structures Model (PSM) with the motion masks, which adopts the temporal information to reduce the searching region and result in a better pose estimation. Moreover, we recognize the human interaction by employing the Hierarchical Hidden Markov Model (HHMM), where the lower level describes the interaction pose for a frame and the higher level describes the human interaction for a sequence. We demonstrate that our system has a better performance in different outdoor scenes and can reach 88% recognition rate.

[1]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[2]  Yi-Ching Liaw,et al.  Video Objects Behavior Recognition Using Fast MHI Approach , 2010, 2010 Seventh International Conference on Computer Graphics, Imaging and Visualization.

[3]  Robert B. Fisher,et al.  Non Parametric Classification of Human Interaction , 2007, IbPRIA.

[4]  Shoab Ahmad Khan,et al.  Moment Invariants Based Human Mistrustful and Suspicious Motion Detection, Recognition and Classification , 2008, Tenth International Conference on Computer Modeling and Simulation (uksim 2008).

[5]  Sangho Park,et al.  Recognition of two-person interactions using a hierarchical Bayesian network , 2003, IWVS '03.

[6]  Deva Ramanan,et al.  Learning to parse images of articulated bodies , 2006, NIPS.

[7]  Chil-Woo Lee,et al.  Pictorial structures-based upper body tracking and gesture recognition , 2011, 2011 17th Korea-Japan Joint Workshop on Frontiers of Computer Vision (FCV).

[8]  J. Aggarwal,et al.  A Bayesian approach to human activity recognition , 1999, Proceedings Second IEEE Workshop on Visual Surveillance (VS'99) (Cat. No.98-89223).

[9]  Daniel P. Huttenlocher,et al.  Pictorial Structures for Object Recognition , 2004, International Journal of Computer Vision.

[10]  Ronghua Luo,et al.  Side view pose estimation of human from images using prior knowledge , 2011, 2011 4th International Congress on Image and Signal Processing.

[11]  Bernt Schiele,et al.  Pictorial structures revisited: People detection and articulated pose estimation , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Chang Woo Lee,et al.  Vision-Based Human Motion Analysis for Event Recognition , 2008, 2008 Second International Symposium on Intelligent Information Technology Application.

[13]  Sergio A. Velastin,et al.  Tracking-based event detection for CCTV systems , 2004, Pattern Analysis and Applications.

[14]  Thomas Hofmann,et al.  Support Vector Machines for Multiple-Instance Learning , 2002, NIPS.

[15]  Chin-Hua Hu,et al.  An efficient method of human behavior recognition in smart environments , 2010, 2010 International Conference on Computer Application and System Modeling (ICCASM 2010).

[16]  Bingbing Ni,et al.  RGBD-HuDaAct: A color-depth video database for human daily activity recognition , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[17]  Tamas Vajda Behavior recognition using Pictorial Structures and DTW , 2010, 2010 IEEE International Conference on Automation, Quality and Testing, Robotics (AQTR).

[18]  Yi Yang,et al.  Articulated pose estimation with flexible mixtures-of-parts , 2011, CVPR 2011.

[19]  Yoram Singer,et al.  The Hierarchical Hidden Markov Model: Analysis and Applications , 1998, Machine Learning.

[20]  Andrew Zisserman,et al.  2D Articulated Human Pose Estimation and Retrieval in (Almost) Unconstrained Still Images , 2012, International Journal of Computer Vision.

[21]  Larry S. Davis,et al.  Towards 3-D model-based tracking and recognition of human movement: a multi-view approach , 1995 .

[22]  Ehud Rivlin,et al.  H-APF: Using hierarchical representation of human body for 3-D articulated tracking and action classification , 2009, 2009 IEEE 12th International Conference on Computer Vision Workshops, ICCV Workshops.

[23]  Thomas B. Moeslund,et al.  Pose Estimation of Interacting People using Pictorial Structures , 2010, 2010 7th IEEE International Conference on Advanced Video and Signal Based Surveillance.

[24]  Martin A. Fischler,et al.  The Representation and Matching of Pictorial Structures , 1973, IEEE Transactions on Computers.

[25]  Tamas Vajda,et al.  Pictorial structure based people detection and pose estimation in videos , 2011, 2011 IEEE 7th International Conference on Intelligent Computer Communication and Processing.

[26]  Jianguo Zhang,et al.  The PASCAL Visual Object Classes Challenge , 2006 .

[27]  Dimitris Samaras,et al.  Two-person interaction detection using body-pose features and multiple instance learning , 2012, 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.