Fast and robust head detection with arbitrary pose and occlusion

Head detection in images and videos plays an important role in a wide range of computer vision and surveillance applications. Aiming to detect heads with arbitrarily occluded faces and head pose, in this paper, we propose a novel Gaussian energy function based algorithm for elliptical head contour detection. Starting with the localization of head and shoulder by an improved Gaussian Mixture Model (GMM) approach, the precise head contour is obtained by making use of the Omega shape formed from the head and shoulder. Experimental results on several benchmark datasets demonstrate the superiority of the proposed idea over the state-of-the-art in both detection accuracy and processing speed, even though there are various types of severe occlusions in faces.

[1]  Ho Gi Jung,et al.  Face occlusion detection by using B-spline active contour and skin color information , 2010, 2010 11th International Conference on Control Automation Robotics & Vision.

[2]  Anil K. Jain,et al.  Face Detection in Color Images , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Zhengrong Yao,et al.  Tracking a Detected Face with Dynamic Programming , 2004, 2004 Conference on Computer Vision and Pattern Recognition Workshop.

[4]  Hatice Gunes,et al.  An accurate algorithm for head detection based on XYZ and HSV hair and skin color models , 2008, 2008 15th IEEE International Conference on Image Processing.

[5]  Jean-Marc Odobez,et al.  A probabilistic framework for joint head tracking and pose estimation , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[6]  Maolin Chen,et al.  Head tracking with shape modeling and detection , 2005, The 2nd Canadian Conference on Computer and Robot Vision (CRV'05).

[7]  Jean-Marc Odobez,et al.  A probabilistic framework for joint head tracking and pose estimation , 2004, ICPR 2004.

[8]  Kazuhiko Yamamoto,et al.  Face and head detection for a real-time surveillance system , 2004, Proceedings of the 17th International Conference on Pattern Recognition, 2004. ICPR 2004..

[9]  Quan Pan,et al.  Real-time head tracking system with an active camera , 2004, Fifth World Congress on Intelligent Control and Automation (IEEE Cat. No.04EX788).

[10]  Erkki Oja,et al.  A new curve detection method: Randomized Hough transform (RHT) , 1990, Pattern Recognit. Lett..

[11]  De Xu,et al.  Real-time elliptical head contour detection under arbitrary pose and wide distance range , 2009, J. Vis. Commun. Image Represent..

[12]  Stanley T. Birchfield,et al.  Elliptical head tracking using intensity gradients and color histograms , 1998, Proceedings. 1998 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No.98CB36231).

[13]  Jong Seok Lim,et al.  Detecting and tracking of multiple pedestrians using motion, color information and the AdaBoost algorithm , 2012, Multimedia Tools and Applications.

[14]  Zhi-Qiang Liu,et al.  A robust, real-time ellipse detector , 2005, Pattern Recognit..

[15]  Ligeng Dong,et al.  Head Pose Estimation Using Covariance of Oriented Gradients , 2010, ICASSP.

[16]  Jing Shen,et al.  Moving Human Head Detection for Automatic Passenger Counting System , 2012 .

[17]  James L. Crowley,et al.  Head Pose Estimation on Low Resolution Images , 2006, CLEAR.

[18]  Wen-Huang Cheng,et al.  Whac-a-mole: A head detection scheme by estimating the 3D envelope from depth image , 2013, 2013 IEEE International Conference on Multimedia and Expo Workshops (ICMEW).

[19]  David R. Bull,et al.  GMM-based efficient foreground detection with adaptive region update , 2009, 2009 16th IEEE International Conference on Image Processing (ICIP).

[20]  Kikuo Fujimura,et al.  A robust elliptical head tracker , 2004, Sixth IEEE International Conference on Automatic Face and Gesture Recognition, 2004. Proceedings..

[21]  Emilio Maggio,et al.  Particle PHD Filtering for Multi-Target Visual Tracking , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[22]  Qi Feng,et al.  An effective head pose estimation approach using Lie Algebrized Gaussians based face representation , 2013, Multimedia Tools and Applications.

[23]  Chi Hau Chen,et al.  Moving Objects Detection and Segmentation In Dynamic Video Backgrounds , 2007, 2007 IEEE Conference on Technologies for Homeland Security.

[24]  Narendra Ahuja,et al.  Detecting Faces in Images: A Survey , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Liyu Gong,et al.  Shape of Gaussians as feature descriptors , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[26]  Rainer Herpers,et al.  Robust Head Detection and Tracking in Cluttered Workshop Environments Using GMM , 2005, DAGM-Symposium.

[27]  Mingai Li,et al.  An efficient moving object detection algorithm based on improved GMM and cropped frame technique , 2012, 2012 IEEE International Conference on Mechatronics and Automation.

[28]  Ho-Sub Yoon,et al.  A robust human head detection method for human tracking , 2006, 2006 IEEE/RSJ International Conference on Intelligent Robots and Systems.

[29]  Qi Feng,et al.  Effective head pose estimation using Lie Algebrized Gaussians , 2013, 2013 IEEE International Conference on Multimedia and Expo (ICME).

[30]  Mohan M. Trivedi,et al.  A two-stage head pose estimation framework and evaluation , 2008, Pattern Recognit..

[31]  P. Jonathon Phillips,et al.  Face recognition based on frontal views generated from non-frontal images , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[32]  Radoslaw Weychan,et al.  Influence of low resolution of images on reliability of face detection and recognition , 2015, Multimedia Tools and Applications.