Human detection using Discriminative and Robust Local Binary Pattern

Despite superior performance of Local Binary Pattern (LBP) in texture classification and face detection, its performance in human detection has been limited for two reasons. Firstly, LBP differentiates a bright human from a dark background and vice-versa. This increases the intra-class variation of humans. Secondly, LBP is contrast and illumination invariant. It does not discriminate between weak contrast local regions and similar strong contrast ones, resulting in a similar feature representation. Non-Redundant LBP (NRLBP) has been proposed to solve the first issue of LBP. However, an inherent limitation of NRLBP is that LBP codes and their complements in the same block are mapped to the same code. Furthermore, NRLBP, like LBP, is also contrast and illumination invariant. In this paper, we propose a novel edge-texture feature, Discriminative Robust Local Binary Pattern (DRLBP), for human detection. DRLBP alleviates the problems of LBP and NRLBP by considering the weighted sum and absolute difference of a LBP code and its complement. Our experimental results show that DRLBP consistently outperforms LBP and NRLBP for human detection.

[1]  Zhenhua Guo,et al.  A Completed Modeling of Local Binary Pattern Operator for Texture Classification , 2010, IEEE Transactions on Image Processing.

[2]  Xudong Jiang,et al.  Extended Histogram of Gradients with Asymmetric Principal Component and Discriminant Analyses for Human Detection , 2011, 2011 Canadian Conference on Computer and Robot Vision.

[3]  Satoshi Goto,et al.  Histogram of template for human detection , 2010, 2010 IEEE International Conference on Acoustics, Speech and Signal Processing.

[4]  Xudong Jiang,et al.  Extended Histogram of Gradients feature for human detection , 2010, 2010 IEEE International Conference on Image Processing.

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

[6]  Ramakant Nevatia,et al.  Detection of multiple, partially occluded humans in a single image by Bayesian combination of edgelet part detectors , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.

[7]  Cordelia Schmid,et al.  Learning to Parse Pictures of People , 2002, ECCV.

[8]  Andrew Zisserman,et al.  Incremental learning of object detectors using a visual shape alphabet , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[9]  Shu Liao,et al.  Dominant Local Binary Patterns for Texture Classification , 2009, IEEE Transactions on Image Processing.

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

[11]  David A. Forsyth,et al.  Probabilistic Methods for Finding People , 2001, International Journal of Computer Vision.

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

[13]  Matti Pietikäinen,et al.  Dynamic Texture Recognition Using Local Binary Patterns with an Application to Facial Expressions , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[14]  Larry S. Davis,et al.  Shape-Based Human Detection and Segmentation via Hierarchical Part-Template Matching , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[15]  Fatih Murat Porikli,et al.  Pedestrian Detection via Classification on Riemannian Manifolds , 2008, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[16]  Matti Pietikäinen,et al.  Face Description with Local Binary Patterns: Application to Face Recognition , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[17]  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).

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

[19]  Xiaoyang Tan,et al.  Enhanced Local Texture Feature Sets for Face Recognition Under Difficult Lighting Conditions , 2007, IEEE Transactions on Image Processing.

[20]  Wanqing Li,et al.  Object detection using Non-Redundant Local Binary Patterns , 2010, 2010 IEEE International Conference on Image Processing.