优化了Viola & Jones人脸检测算法在RISC处理器上的实时实现

M. Benkiniouar, M. Benmohammed
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引用次数: 1

摘要

本文介绍了基于adaboost的Viola & Jones人脸检测算法在RISC处理器上结合遗传方法(称为adaboost /GA)的优化实时实现。该算法对图像或视频序列中的人脸进行检测,在CMU和MIT图像的基础上,检测率达到97%左右。在这项工作中,我们从复杂性和计算,资源消耗和并行性方面对算法进行了研究。我们提出了用于获得处理速率的各种优化,大约40张图像/秒,图像大小为320 × 240像素。这些结果是通过在配备2.0千兆字节RAM的Pentium IV 2.0 GHz处理器上探索各种处理器RISC优化技术(循环解绕,寄存器旋转,并行性,扩展SE…)而获得的
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimisation the real time implementation of the Viola & Jones face detection algorithm on RISC processor
This article describes the optimization real-time implementation of Viola & Jones face detection algorithm, based adaboost, on RISC processor combined with a genetic approach (called AdaBoost/GA). This algorithm detects the faces in image or video sequences, with rate detection about 97% on the basis of image CMU & MIT. In this work we present a study of the algorithm in terms of complexity and calculation, resources consumption and parallelism. We present a various optimizations used to obtain a rate of treatment, about, 40 images/second, and 320 × 240 pixels of image size. These results were obtained by exploring various techniques of optimization on processor RISC (unwinding of loops, rotation of registers, parallelism, extension SE…) on a processor Pentium IV 2.0 GHz and equipped with 2.0 Giga byte of RAM
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