An Enhanced Histogram of Oriented Gradients for Pedestrian Detection

Significant researches have been carried out for pedestrian detection in images. The outstanding Histogram-of-Oriented-Gradients (HOG) feature proposed by Dalal and Triggs is the state-of-art for this task, and it is applied with a linear support vector machine (SVM) in a sliding-window framework. The novel method we proposed in this paper is based on this approach in which we add an enhanced feature to contain more feature information. Besides the same gradient information extraction process as HOG's, the enhanced feature extraction contains two steps: firstly, a new way is found to downscale the gradient image to its quarter size without losing much gradient information; secondly, `Circle HOG' features are extracted from those downscaled images. Then we combine the new enhanced features and the original HOG features together as an Enhanced HOG (EHOG) features. Our method is evaluated with a Histogram Intersection Kernel SVM (HIKSVM) on the public “INRIA” pedestrian detection benchmark dataset. The results show that proposed method consistently improves the detection rate by 4.5% in detection accuracy, compared with the original HOG.

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

[2]  Greg Mori,et al.  Detecting Pedestrians by Learning Shapelet Features , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[3]  Rao Muhammad Anwer,et al.  Opponent Colors for Human Detection , 2011, IbPRIA.

[4]  Theo Gevers,et al.  Improving HOG with Image Segmentation: Application to Human Detection , 2012, ACIVS.

[5]  Jianxin Wu,et al.  A Fast Dual Method for HIK SVM Learning , 2010, ECCV.

[6]  Tomaso A. Poggio,et al.  A Trainable System for Object Detection , 2000, International Journal of Computer Vision.

[7]  Paul A. Viola,et al.  Detecting Pedestrians Using Patterns of Motion and Appearance , 2005, International Journal of Computer Vision.

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

[9]  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).

[10]  Jitendra Malik,et al.  Matching Shapes , 2001, ICCV.

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

[12]  Larry D. Hostetler,et al.  The estimation of the gradient of a density function, with applications in pattern recognition , 1975, IEEE Trans. Inf. Theory.

[13]  Chih-Jen Lin,et al.  LIBLINEAR: A Library for Large Linear Classification , 2008, J. Mach. Learn. Res..

[14]  Luc Van Gool,et al.  Pedestrian detection at 100 frames per second , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[15]  Rao Muhammad Anwer,et al.  Color Contribution to Part-Based Person Detection in Different Types of Scenarios , 2011, CAIP.

[16]  Bernt Schiele,et al.  A Performance Evaluation of Single and Multi-feature People Detection , 2008, DAGM-Symposium.

[17]  Subhransu Maji,et al.  Classification using intersection kernel support vector machines is efficient , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Satoshi Ito,et al.  Co-occurrence Histograms of Oriented Gradients for Pedestrian Detection , 2009, PSIVT.

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

[20]  Jing Xiao,et al.  Contextual boost for pedestrian detection , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[21]  Mark Everingham,et al.  Implicit color segmentation features for pedestrian and object detection , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[22]  David Vázquez,et al.  Random Forests of Local Experts for Pedestrian Detection , 2013, 2013 IEEE International Conference on Computer Vision.

[23]  Bill Triggs,et al.  Feature Sets and Dimensionality Reduction for Visual Object Detection , 2010, BMVC.

[24]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[25]  Pietro Perona,et al.  The Fastest Pedestrian Detector in the West , 2010, BMVC.

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

[27]  Ramakant Nevatia,et al.  Optimizing discrimination-efficiency tradeoff in integrating heterogeneous local features for object detection , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.