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Hardware Designs for Histogram of Oriented Gradients in Pedestrian Detection: A Survey

Publication Type : Conference Paper

Publisher : Fifth International Conference on Advanced Computing and Communication Systems

Source : Fifth International Conference on Advanced Computing and Communication Systems, March 15 – 16, 2019

Url : https://ieeexplore.ieee.org/document/8728486

Campus : Coimbatore

School : School of Engineering

Department : Computer Science and Engineering

Year : 2019

Abstract : Feature detection, narrowed down to pedestrian detection, is an imperative domain where automated applications such as Automatic Driver Support Systems, Robotics and similar image vision and machine vision technologies. Histogram of Oriented Gradients (HOG) is a robust, scalable and efficient feature extraction method that works on luminance gradients among neighboring pixels. The extracted feature is normalized and classified through support vector machines (SVM). Improvements in the design through approximate computations, parallelism and pipelining applied to SVM classification and histogram generation, Parallel implementation of entire HOG and exploration of possible applications of the algorithm. This paper cites the software improvements of HOG and hardware implementations targeted on FPGA for variations of HOG.

Cite this Research Publication : Bagavathi C and Saraniya O “Hardware Designs for Histogram of Oriented Gradients in Pedestrian Detection: A Survey” in Fifth International Conference on Advanced Computing and Communication Systems, March 15 – 16, 2019.

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