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论文类型：期刊论文

发表时间：2012-03-01

发表刊物：EXPERT SYSTEMS WITH APPLICATIONS

收录刊物：SCIE、EI

卷号：39

期号：4

页面范围：4274-4286

ISSN号：0957-4174

关键字：Pedestrian detection; Two-stage classifier; Feature extraction; Support vector machine

摘要：Pedestrians are the vulnerable participants in transportation system when crashes happen. It is important to detect pedestrian efficiently and accurately in many computer vision applications, such as intelligent transportation systems (ITSs) and safety driving assistant systems (SDASs). This paper proposes a two-stage pedestrian detection method based on machine vision. In the first stage, AdaBoost algorithm and cascading method are adopted to segment pedestrian candidates from image. To confirm whether each candidate is pedestrian or not, a second stage is needed to eliminate some false positives. In this stage, a pedestrian recognizing classifier is trained with support vector machine (SVM). The input features used for SVM training are extracted from both the sample gray images and edge images. Finally, the performance of the proposed pedestrian detection method is tested with real-world data. Results show that the performance is better than conventional single-stage classifier, such as AdaBoost based or SVM based classifier. (C) 2011 Elsevier Ltd. All rights reserved.