Pedestrian detection with image segmentation and virtual mask

Pedestrian detection is one of the most popular research areas in video processing and it is vital for video surveillance systems. In this paper, we present a real-time pedestrian detection system based on Dalal and Triggs's human detection framework with the use of image segmentation and virtual mask. Image segmentation enables the system to focus only on the region of interest whereas the virtual mask reduces noise and unnecessary processing. The system has high computational speed while it retains the discriminative power of Histogram of Oriented Gradient (HOG) features for pedestrian detection. It is tested with videos captured in a single task environment and in various lengths. The experimental results showed that most of the pedestrians in the videos are correctly detected, achieving an overall accuracy of 83.29% with low amount of computational time, which implies the effectiveness in detecting pedestrians and the real-time property of the proposed system.

[1]  Cordelia Schmid,et al.  Human Detection Using Oriented Histograms of Flow and Appearance , 2006, ECCV.

[2]  Jianpeng Zhou,et al.  Real Time Robust Human Detection and Tracking System , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Workshops.

[3]  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).

[4]  Rainer Lienhart,et al.  An extended set of Haar-like features for rapid object detection , 2002, Proceedings. International Conference on Image Processing.

[5]  C. Rosenberger,et al.  Comparative Study on Foreground Detection Algorithms for Human Detection , 2007, Fourth International Conference on Image and Graphics (ICIG 2007).

[6]  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).

[7]  Yu-Jin Zhang,et al.  Fast Human Detection by Boosting Histograms of Oriented Gradients , 2007, Fourth International Conference on Image and Graphics (ICIG 2007).

[8]  Dariu Gavrila,et al.  An Experimental Study on Pedestrian Classification , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[9]  Satoshi Goto,et al.  Human detection using motion and appearance based feature , 2009, 2009 7th International Conference on Information, Communications and Signal Processing (ICICS).

[10]  Li Hai,et al.  Characteristic Preserving Binarization for Fingerprint Image , 2007, Fourth International Conference on Image and Graphics (ICIG 2007).

[11]  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.