On recognition of human orientation with respect to a static camera

Detection of orientation of a human being with respect to a static camera is an important problem in computer vision. This paper proposes a method for detection of orientation of a human being from four different orientations (e.g. front, side-left, side-right, back) with respect to a static camera. In the proposed method, extraction of features of a human being has been performed in terms of boundary description known as the signature. A template database of four different human orientations has been created. The extracted features of a testing sample have been compared with that stored in the template database and a dissimilarity value has been calculated. The classification has been performed using the dissimilarity value as a metric. The results obtained are encouraging.

[1]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[2]  Nabendu Chaki,et al.  Recognition of object orientation from images , 2012, 2012 International Conference on Emerging Trends in Science, Engineering and Technology (INCOSET).

[3]  Eyup Gedikli,et al.  Silhouette Based Human Motion Detection and Analysis for Real-Time Automated Video Surveillance , 2005 .

[4]  Rafael C. González,et al.  Local Determination of a Moving Contrast Edge , 1985, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[5]  K. Nakamura,et al.  Recognition of object orientation and shape by a rotation spreading associative neural network , 2001, IJCNN'01. International Joint Conference on Neural Networks. Proceedings (Cat. No.01CH37222).

[6]  Xiaoli Sun,et al.  Object orientation algorithm for sequence images based on adaboost classification , 2009, 2009 ISECS International Colloquium on Computing, Communication, Control, and Management.

[7]  Kpalma Kidiyo,et al.  A Survey of Shape Feature Extraction Techniques , 2008 .

[8]  Chyi-Yeu Lin,et al.  Object orientation recognition based on SIFT and SVM by using stereo camera , 2009, 2008 IEEE International Conference on Robotics and Biomimetics.

[9]  Nabendu Chaki,et al.  A Low Cost Moving Object Detection Method Using Boundary Tracking , 2011 .

[10]  Jitendra Malik,et al.  Estimating Human Body Configurations Using Shape Context Matching , 2002, ECCV.

[11]  Martial Hebert,et al.  Incorporating Background Invariance into Feature-Based Object Recognition , 2005, 2005 Seventh IEEE Workshops on Applications of Computer Vision (WACV/MOTION'05) - Volume 1.

[12]  Jitendra Malik,et al.  Efficient shape matching using shape contexts , 2005, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Ulrich Eckhardt,et al.  Shape descriptors for non-rigid shapes with a single closed contour , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

[14]  Guojun Lu,et al.  Review of shape representation and description techniques , 2004, Pattern Recognit..

[15]  Stelios Krinidis,et al.  Frequency-based object orientation and scaling determination , 2006, 2006 IEEE International Symposium on Circuits and Systems.