HOG feature extractor circuit for real-time human and vehicle detection

Smart vehicle technologies such as ADAS are growing concern about. Especially, pedestrian and vehicle recognition system based on machine vision is a big issue. In this paper, we propose the hardwired HOG feature extractor circuit for real-time human and vehicle detection, and describe the hardware implementation results. Our HOG feature extractor supports weighted gradient value, 2D histogram interpolation and block normalization. We have used the simplified methods of the square root and division operation for the hardware implementation. Our HOG feature extractor circuit was verified on FPGA environment and can be processed 33 frames per seconds for 640×480 VGA images in real-time.

[1]  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).

[2]  E. Rückert.  Detecting Pedestrians by Learning Shapelet Features , 2007 .

[3]  Tom Wilson,et al.  Pedestrian detection implemented on a fixed-point parallel architecture , 2009, 2009 IEEE 13th International Symposium on Consumer Electronics.

[4]  Yuichiro Shibata,et al.  Deep pipelined one-chip FPGA implementation of a real-time image-based human detection algorithm , 2011, 2011 International Conference on Field-Programmable Technology.

[5]  Yan-ping Chen,et al.  Fast hog feature computation based on CUDA , 2011, 2011 IEEE International Conference on Computer Science and Automation Engineering.

[6]  Takashi Machida,et al.  GPU & CPU cooperative accelerated pedestrian and vehicle detection , 2011, 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops).

[7]  Ichiro Masaki,et al.  Fast human detection with cascaded ensembles on the GPU , 2010, 2010 IEEE Intelligent Vehicles Symposium.

[8]  Paul A. Viola,et al.  Detecting Pedestrians Using Patterns of Motion and Appearance , 2003, Proceedings Ninth IEEE International Conference on Computer Vision.

[9]  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.

[10]  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).

[11]  Ramakant Nevatia,et al.  Efficient scan-window based object detection using GPGPU , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

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

[13]  Ulrich Brunsmann,et al.  FPGA-GPU architecture for kernel SVM pedestrian detection , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Workshops.