Pedestrian detection based on multifractal analysis

This paper proposes a new multifractal analysis based feature representation for object representation. Multiple Fractal Dimensions (MFD) are calculated to describe the distribution of fractal dimensions measured on a finite number of point sets extracted from the image. The proposed MFD feature is theoretically proven to be invariant to articulations, which is a desirable characteristic for faces and pedestrian due to the existence of expressions, posture and illumination variations. The new object representation is extensively evaluated on pedestrian detection problem. The experiments INRIA pedestrian databases show that our method achieves a much better performance than baseline methods in terms of recognition rates.

[1]  Ran Xu,et al.  Cascaded L1-norm Minimization Learning (CLML) classifier for human detection , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

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

[4]  Paul A. Viola,et al.  Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[5]  Toshiro Kubota,et al.  Wavelet-Based Fractal Signature Analysis forAutomatic Target , 1998 .

[6]  Lance M. Kaplan Extended fractal analysis for texture classification and segmentation , 1999, IEEE Trans. Image Process..

[7]  Yong Xu,et al.  Viewpoint Invariant Texture Description Using Fractal Analysis , 2009, International Journal of Computer Vision.

[8]  Rongrong Ji,et al.  Bounding Multiple Gaussians Uncertainty with Application to Object Tracking , 2016, International Journal of Computer Vision.

[9]  Chih-Jen Lin,et al.  LIBSVM: A library for support vector machines , 2011, TIST.

[10]  Fatih Murat Porikli,et al.  Human Detection via Classification on Riemannian Manifolds , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[11]  Timothy R. McJunkin,et al.  Suboptimal Minimum Cluster Volume Cover-Based Method for Measuring Fractal Dimension , 2003, IEEE Trans. Pattern Anal. Mach. Intell..

[12]  Joseph Naor,et al.  Multiple Resolution Texture Analysis and Classification , 1984, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[13]  Aura Conci,et al.  Multifractal characterization of texture-based segmentation , 2000, Proceedings 2000 International Conference on Image Processing (Cat. No.00CH37101).

[14]  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).

[15]  Lui Kam,et al.  Are multifractal multipermuted multinomial measures good enough for unsupervised image segmentation? , 2000, Proceedings IEEE Conference on Computer Vision and Pattern Recognition. CVPR 2000 (Cat. No.PR00662).

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

[17]  Alessio Del Bue,et al.  Adaptive Local Movement Modelling for Object Tracking , 2015, 2015 IEEE Winter Conference on Applications of Computer Vision.

[18]  Chen Chen,et al.  Output Constraint Transfer for Kernelized Correlation Filter in Tracking , 2016, IEEE Transactions on Systems, Man, and Cybernetics: Systems.

[19]  Mei-Chen Yeh,et al.  Fast Human Detection Using a Cascade of Histograms of Oriented Gradients , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

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