基于CapsNet的行人检测方法及评价

交通运输系统工程与信息 ›› 2019, Vol. 19 ›› Issue (3): 54-61.

• 智能交通系统与信息技术 • 上一篇下一篇

基于CapsNet的行人检测方法及评价

邵毅明，屈治华，邓天民^*，朱杰

重庆交通大学交通运输学院，重庆 400074

收稿日期:2018-11-14 修回日期:2019-01-20 出版日期:2019-06-25 发布日期:2019-06-25
作者简介:邵毅明(1955-)，男，四川资阳人，教授，博士.
基金资助:
国家重点研发计划/ National Key Research and Development Program of China(2016YFB0100905)；重庆市科技人才培养计划/ Chongqing Science and Technology Talent Training Program(cstc2013kjrc-qnrc0148).

Pedestrian Detection and Evaluation Based on CapsNet

SHAO Yi-ming, QU Zhi-hua, DENG Tian-min, ZHU Jie

School of Traffic and Transportation, Chongqing Jiaotong University, Chongqing 400074, China

Received:2018-11-14 Revised:2019-01-20 Online:2019-06-25 Published:2019-06-25

摘要/Abstract

摘要：

为了提高道路环境中行人目标检测的准确率，改善现有检测算法对不同环境视角下漏检率较高、耗时过长、实用性较差等问题，本文提出了一种基于 CapsNet的行人检测模型. CapsNet由神经元所构成的 Capsule组成，通过动态路由协议对物体的实例化参数进行表达和传递，保留了各特征对象间的空间层级，采用 Caltech公开数据库对所提算法的有效性进行验证，并在检测准确率及算法耗时等方面与其他算法进行对比.实验结果表明：相比于其他主流检测算法，本文算法在确保检测效率的前提下，对数平均漏检率最低可降至 9.17%；且在 Caltech、INRIA和 NICTA数据集的交叉验证实验中，也能达到良好的检测效果，具有较好的鲁棒性和泛化能力.

关键词: 智能交通, 行人检测, CapsNet, 机器视觉, 深度学习

Abstract:

To enhance the detection accuracy of pedestrian target in the road environment, and solve the problems in the existing detection algorithms such as high undetected rate, long time- consumption and poor practicability under different environmental perspectives, a CapsNet-based pedestrian detection model is presented in this paper. CapsNet, consisted of a Capsule composed of neurons, expresses and transfers the instantiation parameters of the object through the dynamic routing protocol while preserving the spatial hierarchy between the feature objects, verifies the validity of the proposed algorithm based on the Caltech public database, and compares with other algorithms in terms of detection accuracy and time consumption of the algorithms. The experimental results show that compared with other mainstream detection algorithms, the algorithm can reduce the undetected rate to a minimum of 9.17% under the premise of ensuring the detection efficiency, and in the cross-validation experiment of Caltech, INRIA and NICTA data sets, good detection results can be achieved, with remarkable robustness and generalization ability.

Key words: intelligent transportation, pedestrian detection, CapsNet, machine vision, deep learning

中图分类号:

TP391.41

邵毅明，屈治华，邓天民，朱杰. 基于CapsNet的行人检测方法及评价[J]. 交通运输系统工程与信息, 2019, 19(3): 54-61.

SHAO Yi-ming, QU Zhi-hua, DENG Tian-min, ZHU Jie. Pedestrian Detection and Evaluation Based on CapsNet[J]. Journal of Transportation Systems Engineering and Information Technology, 2019, 19(3): 54-61.

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链接本文: http://www.tseit.org.cn/CN/abstract/abstract19835.shtml

http://www.tseit.org.cn/CN/Y2019/V19/I3/54

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基于CapsNet的行人检测方法及评价

Pedestrian Detection and Evaluation Based on CapsNet

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