{"title":"Optimisation the real time implementation of the Viola & Jones face detection algorithm on RISC processor","authors":"M. Benkiniouar, M. Benmohammed","doi":"10.1109/SM2ACD.2010.5672301","DOIUrl":null,"url":null,"abstract":"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","PeriodicalId":442381,"journal":{"name":"2010 XIth International Workshop on Symbolic and Numerical Methods, Modeling and Applications to Circuit Design (SM2ACD)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 XIth International Workshop on Symbolic and Numerical Methods, Modeling and Applications to Circuit Design (SM2ACD)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SM2ACD.2010.5672301","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1
Abstract
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