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Pedestrian Detection on FPGA
Mid Sweden University, Faculty of Science, Technology and Media, Department of Electronics Design.
2014 (English)Independent thesis Advanced level (degree of Master (One Year)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Image processing emerges from the curiosity of human vision. To translate, what we see in everyday life and how we differentiate between objects, to robotic vision is a challenging and modern research topic. This thesis focuses on detecting a pedestrian within a standard format of an image. The efficiency of the algorithm is observed after its implementation in FPGA. The algorithm for pedestrian detection was developed using MATLAB as a base. To detect a pedestrian, a histogram of oriented gradient (HOG) of an image was computed. Study indicates that HOG is unique for different objects within an image. The HOG of a series of images was computed to train a binary classifier. A new image was then fed to the classifier in order to test its efficiency. Within the time frame of the thesis, the algorithm was partially translated to a hardware description using VHDL as a base descriptor. The proficiency of the hardware implementation was noted and the result exported to MATLAB for further processing. A hybrid model was created, in which the pre-processing steps were computed in FPGA and a classification performed in MATLAB. The outcome of the thesis shows that HOG is a very efficient and effective way to classify and differentiate different objects within an image. Given its efficiency, this algorithm may even be extended to video.

Place, publisher, year, edition, pages
2014. , 58 p.
Keyword [en]
Machine Vision, Image Processing, HOG, VHDL, FPGA, MATLAB, Pedestrian Detection
National Category
Signal Processing
URN: urn:nbn:se:miun:diva-21509OAI: diva2:702896
Subject / course
Electrical Engineering ET2
Educational program
International Master's Programme in Electronics Design TELAA 120 higher education credits
2014-01-07, Mittuniversitetet, Holmgatan, Sundsvall, 12:30 (English)
Available from: 2014-03-05 Created: 2014-03-04 Last updated: 2014-03-05Bibliographically approved

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