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首页> 外文会议>International Symposium on Telecommunications >Pedestrian Detection Using an Extended Fast RCNN based on a Secure Margin in RoI Feature Maps
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Pedestrian Detection Using an Extended Fast RCNN based on a Secure Margin in RoI Feature Maps

机译：使用RoI特征图中基于安全裕度的扩展快速RCNN进行行人检测

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Pedestrian Detection based on Deep Convolutional Neural Network (DCNN) has recently gained a great deal of attention. Most of the proposed CNN based methods train networks employing either of the well-known Region-based CNN (RCNN) or Fast Region-based CNN (FRCNN) approaches. In this paper, we present a novel method to train Deep CNN. This method is based on an extended and improved FRCNN for pedestrian detection. It performs both classification and bounding-box regression more accurately. The proposed approach takes the advantage of a Secure Margin in Region of Interest (SM-RoI) to create multi-RoIs. Then based on some criteria, it chooses one of the RoIs with the highest score. The bounding-box extracted from the proposed FRCNN-SM approach is more effective than that of FRCNN approach in fitting and covering pedestrian. Evaluated on Caltech dataset, our proposed approach detects pedestrian more accurately than RCNN and FRCNN approaches.
机译：基于深度卷积神经网络（DCNN）的行人检测最近引起了广泛的关注。大多数提出的基于CNN的方法训练网络使用众所周知的基于区域的CNN（RCNN）或基于快速区域的CNN（FRCNN）方法。在本文中，我们提出了一种训练深度CNN的新方法。该方法基于对行人检测的扩展和改进的FRCNN。它可以更准确地执行分类和边界框回归。所提出的方法利用了感兴趣区域安全保证金（SM-RoI）的优势来创建多条RoI。然后根据一些标准，选择得分最高的投资回报率之一。从提出的FRCNN-SM方法中提取的边界框在拟合和覆盖行人方面比FRCNN方法更有效。根据Caltech数据集进行评估，我们提出的方法比RCNN和FRCNN方法更准确地检测行人。

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