Design of unified support vector machine circuit for pedestrians and cars detection

This paper describes the design of unified support vector machine circuit for pedestrians and cars detection. By unifying the algorithms and architectures of linear and nonlinear SVM classifications, the proposed circuit can support both linear and non-linear classifications very efficiently in term...

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Bibliographic Details
Main Authors: Soojin Kim, Seonyoung Lee, Kyoungwon Min, Kyeongsoon Cho
Format: Conference Proceeding
Language:English
Subjects:
Calculators
Classification algorithms
Clocks
Conferences
Kernel
Pipelines
Support vector machines
Online Access:Request full text
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Summary:This paper describes the design of unified support vector machine circuit for pedestrians and cars detection. By unifying the algorithms and architectures of linear and nonlinear SVM classifications, the proposed circuit can support both linear and non-linear classifications very efficiently in terms of circuit size and performance. The circuit size is minimized by sharing most of the resources required in the computation for both classification types. Parallel architecture with pipeline is adopted to accelerate the processing speed to handle a large amount of operations for real-time processing. 48×96 and 64×64 sliding windows with 6 window strides are used to detect pedestrians and cars, respectively. The synthesized circuit using 65nm standard cell library consists of 848,349 gates and its maximum operating frequency is 435MHz. The circuit can process 91.9 640×480 image frames per second assuming three cameras equipped on front, right and left side positions of the vehicle.
DOI:10.1109/NEWCAS.2012.6328952