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Volume: 33 | Article ID: art00009
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Accelerated HOG+SVM for Object Recognition
  DOI :  10.2352/ISSN.2470-1173.2021.6.IRIACV-317  Published OnlineJanuary 2021
Abstract

A novel acceleration strategy is presented for computer vision and machine learning field from both algorithmic and hardware implementation perspective. With our approach, complex mathematical functions such as multiplication can be greatly simplified. As a result, an accelerated machine learning method requires no more than ADD operations, which tremendously reduces processing time, hardware complexity and power consumption. The applicability is illustrated by going through a machine learning example of HOG+SVM, where the accelerated version achieves comparable accuracy based on real datasets of human figure and digits.

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Lilong Shi, Chunji Wang, Yibing Wang, Kwang Oh Kim, "Accelerated HOG+SVM for Object Recognitionin Proc. IS&T Int’l. Symp. on Electronic Imaging: Intelligent Robotics and Industrial Applications using Computer Vision,  2021,  pp 317-1 - 317-3,  https://doi.org/10.2352/ISSN.2470-1173.2021.6.IRIACV-317

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