Fast Pedestrian Detection Using Histogram of Oriented Gradients and Principal Components Analysis

In this paper, we propose a fast and accurate system for detecting pedestrians from a static image. Histogram of Oriented Gradients (HOG) is a well-known feature for pedestrian detection systems but extracting HOG is expensive due to its high dimensional vector. It will cause long processing time and large memory consumption in case of making a pedestrian detection system on high resolution image or video. In order to deal with this problem, we use Principal Components Analysis (PCA) technique to reduce the dimensionality of HOG. The output of PCA will be input for a linear SVM classifier for learning and testing. The experiment results showed that our proposed method reduces processing time but still maintains the similar detection rate. We got twenty five times faster than original HOG feature.

[1]  Saeid Nahavandi,et al.  Human action recognition based on Pyramid Histogram of Oriented Gradients , 2011, 2011 IEEE International Conference on Systems, Man, and Cybernetics.

[2]  François Brémond,et al.  Tracking HoG Descriptors for Gesture Recognition , 2009, 2009 Sixth IEEE International Conference on Advanced Video and Signal Based Surveillance.

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

[4]  Sang Ho Kim,et al.  Understanding the Film As A Public Space: The Public Sphere and the Korean Film Industry in the 1980s , 2013 .

[5]  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).

[6]  James J. Little,et al.  Simultaneous Tracking and Action Recognition using the PCA-HOG Descriptor , 2006, The 3rd Canadian Conference on Computer and Robot Vision (CRV'06).

[7]  Ankur Agarwal,et al.  A Local Basis Representation for Estimating Human Pose from Cluttered Images , 2006, ACCV.

[8]  Larry S. Davis,et al.  Bilattice-based Logical Reasoning for Human Detection , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[9]  Ramakant Nevatia,et al.  Detection and Tracking of Multiple Humans with Extensive Pose Articulation , 2007, 2007 IEEE 11th International Conference on Computer Vision.

[10]  Nathan Halko,et al.  An Algorithm for the Principal Component Analysis of Large Data Sets , 2010, SIAM J. Sci. Comput..

[11]  Tetsuya Takiguchi,et al.  3D human posture estimation using the HOG features from monocular image , 2008, 2008 19th International Conference on Pattern Recognition.

[12]  Takio Kurita,et al.  Selection of Histograms of Oriented Gradients Features for Pedestrian Detection , 2007, ICONIP.

[13]  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).

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

[15]  Dariu Gavrila,et al.  Monocular Pedestrian Detection: Survey and Experiments , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

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

[17]  Alex Pentland,et al.  Face recognition using eigenfaces , 1991, Proceedings. 1991 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[18]  Nathan Halko,et al.  Finding Structure with Randomness: Probabilistic Algorithms for Constructing Approximate Matrix Decompositions , 2009, SIAM Rev..

[19]  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.

[20]  Piotr Bilinski,et al.  Multiple object tracking with occlusions using HOG descriptors and multi resolution images , 2009, ICDP.

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

[22]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[23]  Tomaso A. Poggio,et al.  A general framework for object detection , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[24]  Ji-Hye Kim,et al.  Relationship of Physical Impairment, function and Insulin Resistance in stroke patients , 2013 .

[25]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .