期刊信息
- 刊名： 河北师范大学学报（自然科学版）Journal of Hebei Normal University (Natural Science)
- 主办： 河北师范大学
- ISSN： 1000-5854
- CN： 13-1061/N
- 中国科技核心期刊
- 中国期刊方阵入选期刊
- 中国高校优秀科技期刊
- 华北优秀期刊
- 河北省优秀科技期刊
一种基于场景规则与深度卷积神经网络的行人检测方法
- 淮北师范大学 物理与电子信息学院, 安徽 淮北 235000
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DOI：10.13763/j.cnki.jhebnu.nse.2020.02.006
A Pedestrian Detection Method Based on Scene Rules and Depth Convolutional Neural Network
摘要/Abstract
深度卷积网络是解决分类问题的一种有效手段，但行人检测任务并不能通过分类来直接实现.为了在行人检测问题中进一步发挥深度卷积网络的优越分类性能，在实拍场景下，针对平直道路的情况，提出了一种基于摄像机安装位置和摄像机参数的感兴趣区域分割方法，合理利用先验知识和规则，对行人在图像当中可能出现的位置，以及不同位置上行人的尺度大小给出限制，从而系统仅对可能发生危险的区域进行搜索，避免了传统方法中多尺度遍历搜索整副图像的弊端.在此基础上，将危险区域所得的候选目标窗口作为待检测样本传送到构建好的深度卷积网络中进行分类，完成行人检测任务.实验结果表明，所研究的算法在一定距离内达到了预期的检测效果.
The depth convolutional neural network (DCNN) is an effective technique for the classification problem.However,it cannot be applied directly in the pedestrian detection.To investigate DCNN in pedestrian detection task,in this paper,we proposed a method based on the installation position and parameters of the camera to segment the region of interest (ROI) under the straight road in real scene.The method of scene rules limit the location and size of pedestrians in the image,and the system only searches the possible dangerous area,avoiding the disadvantages of traditional methods which need multistage traverse the whole image.Based on it,the samples collected by this method from the dangerous areas are sent to a depth network,to finish the pedestrian detection task.The experimental results show that the algorithm achieves desired detection results within a certain distance.
关键词
参考文献 10
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