Two Person Interaction Detection Using Kinect Sensor

This proposed work explains a noble two-person interaction modelling system using Kinect sensor. Here a pentagon for each person is formed taking the three dimensional co-ordinate information with the help of Microsoft’s Kinect sensor. Five Euclidean distances between two pentagon vertices corresponding to two persons are considered as features for each frame. So the body gestures of two persons are analysed employing pentagons. Based on these, eight interactions between two persons are modelled. This system produces the best recognition rate (greater than 90 %) with the virtue of multi-class support vector machine for rotation invariance case and for rotation variance phenomenon, the recognition rate is greater than 80 %.

[1]  Zhengyou Zhang,et al.  Microsoft Kinect Sensor and Its Effect , 2012, IEEE Multim..

[2]  Justin Clark,et al.  Object Digitization for Everyone , 2011, Computer.

[3]  Gian Luca Foresti,et al.  Tracking Human Motion from Monocular Sequences , 2008, Int. J. Image Graph..

[4]  Thomas G. Dietterich.  Approximate Statistical Tests for Comparing Supervised Classification Learning Algorithms , 1998, Neural Computation.

[5]  H. Nagel,et al.  Tracking of persons in monocular image sequences , 1997, Proceedings IEEE Nonrigid and Articulated Motion Workshop.

[6]  Tieniu Tan,et al.  Recent developments in human motion analysis , 2003, Pattern Recognit..

[7]  Peter E. Hart,et al.  Nearest neighbor pattern classification , 1967, IEEE Trans. Inf. Theory.

[8]  Kiyoharu Aizawa,et al.  Enhancement of Depth Maps With Alpha Channel Estimation for 3-D Video , 2012, IEEE Journal of Selected Topics in Signal Processing.

[9]  Robert P. W. Duin,et al.  Support vector domain description , 1999, Pattern Recognit. Lett..

[10]  Thomas B. Moeslund,et al.  A Survey of Computer Vision-Based Human Motion Capture , 2001, Comput. Vis. Image Underst..

[11]  Juho Kannala,et al.  Joint Depth and Color Camera Calibration with Distortion Correction , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[12]  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.

[13]  Baining Guo,et al.  Kinect Identity: Technology and Experience , 2011, Computer.

[14]  Sergios Theodoridis,et al.  Introduction to Pattern Recognition: A Matlab Approach , 2010 .

[15]  John Solaro,et al.  The Kinect Digital Out-of-Box Experience , 2011, Computer.

[16]  Seong-Whan Lee,et al.  Gesture Spotting and Recognition for Human–Robot Interaction , 2007, IEEE Transactions on Robotics.

[17]  F. Ryden,et al.  Tech to the Future: Making a "Kinection" with Haptic Interaction , 2012, IEEE Potentials.

[18]  Amit Konar,et al.  Computational Intelligence: Principles, Techniques and Applications , 2005 .

[19]  D. Coomans,et al.  Alternative k-nearest neighbour rules in supervised pattern recognition : Part 1. k-Nearest neighbour classification by using alternative voting rules , 1982 .

[20]  Ligang Liu,et al.  Scanning 3D Full Human Bodies Using Kinects , 2012, IEEE Transactions on Visualization and Computer Graphics.

[21]  Ankur Agarwal,et al.  Recovering 3D human pose from monocular images , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[22]  Desire L. Massart,et al.  Alternative k-nearest neighbour rules in supervised pattern recognition : Part 2. Probabilistic classification on the basis of the kNN method modified for direct density estimation , 1982 .

[23]  Jake K. Aggarwal,et al.  Human motion analysis: a review , 1997, Proceedings IEEE Nonrigid and Articulated Motion Workshop.