HUMAN DETECTION BY USING CENTRIST FEATURES FOR THERMAL IMAGES

In this paper, we present a new human detection scheme for thermal images by using CENsus TRansform hISTogram (CENTRIST) features and Support Vector Machines (SVMs). Human detection in a thermal image is a difficult task due to low image resolution, thermal noising, lack of color, and poor texture information. For thermal images, contour is one of the most useful and discriminative information, so capturing it efficiently is important. Histogram of Oriented Gradient (HOG) is still the most proven way to capture the human contour. CENTRIST is a computationally efficient technique to capture contour cues as compared to HOG, but so far no one has implemented and tested the accuracy of CENTRIST descriptor for infrared thermal images. We developed CENTRIST based human detection system for thermal images and tested its variants. We also made a new dataset of thermal images, since there was no realistic dataset. Experimental results show that CENTRIST exhibits better detection accuracy than HOG, while reducing the training and the testing time significantly.

[1]  Shuicheng Yan,et al.  Discriminative local binary patterns for human detection in personal album , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[2]  Michael Arens,et al.  Feature based person detection beyond the visible spectrum , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[3]  Bernt Schiele,et al.  New features and insights for pedestrian detection , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[4]  Larry S. Davis,et al.  Human detection using partial least squares analysis , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[5]  Federico Girosi,et al.  Training support vector machines: an application to face detection , 1997, Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[6]  Ramakant Nevatia,et al.  Detection and Tracking of Multiple, Partially Occluded Humans by Bayesian Combination of Edgelet based Part Detectors , 2007, International Journal of Computer Vision.

[7]  Pietro Perona,et al.  Integral Channel Features , 2009, BMVC.

[8]  Matti Pietikäinen,et al.  Discriminative features for texture description , 2012, Pattern Recognit..

[9]  Subhransu Maji,et al.  Max-margin additive classifiers for detection , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[10]  James M. Rehg,et al.  CENTRIST: A Visual Descriptor for Scene Categorization , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[11]  David A. McAllester,et al.  A discriminatively trained, multiscale, deformable part model , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Bernt Schiele,et al.  Pedestrian detection in crowded scenes , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[13]  Alberto Broggi,et al.  Pedestrian detection in infrared images , 2003, IEEE IV2003 Intelligent Vehicles Symposium. Proceedings (Cat. No.03TH8683).

[14]  Shaoyan Zhang,et al.  Face recognition with support vector machine , 2003, IEEE International Conference on Robotics, Intelligent Systems and Signal Processing, 2003. Proceedings. 2003.

[15]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[16]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.

[17]  Pietro Perona,et al.  Pedestrian Detection: An Evaluation of the State of the Art , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  Alexandrina Rogozan,et al.  Intensity self similarity features for pedestrian detection in Far-Infrared images , 2012, 2012 IEEE Intelligent Vehicles Symposium.

[19]  Shuicheng Yan,et al.  An HOG-LBP human detector with partial occlusion handling , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[20]  Matti Pietikäinen,et al.  A comparative study of texture measures with classification based on featured distributions , 1996, Pattern Recognit..

[21]  James M. Rehg,et al.  Real-time human detection using contour cues , 2011, 2011 IEEE International Conference on Robotics and Automation.

[22]  Tomaso A. Poggio,et al.  Example-Based Object Detection in Images by Components , 2001, IEEE Trans. Pattern Anal. Mach. Intell..