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Optimisation the real time implementation of the Viola Jones face detection algorithm on RISC processor

机译:优化RISC处理器中提琴和琼斯脸部检测算法的实时实现

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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
机译:本文介绍了基于RISC处理器的基于Viola&Jones脸部检测算法的优化实时实施,RISC处理器与遗传方法(称为Adaboost / Ga)相结合。该算法检测图像或视频序列中的面部,基于图像CMU和MIT的速率检测约97%。在这项工作中,我们在复杂性和计算,资源消耗和并行性方面提出了对算法的研究。我们介绍了用于获得治疗率,约40个图像/秒和320×240像素的各种优化。这些结果是通过探索处理器RISC的各种优化技术(在处理器Pentium IV 2.0GHz上的循环,寄存器,并行,扩展SE ...)上的各种技术获得,并配备了2.0千兆字节的RAM

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