Small-Scale Pedestrian Detection Based on Deep Neural Network
Small-Scale Pedestrian Detection Based on Deep Neural Network
复制标题
基于深度神经网络的小范围行人检测
DOI：
10.1109/tits.2019.2923752
复制
发表时间：
2020-07-01
影响因子：
8.5
通讯作者：
Gao, Xinbo
中科院分区：
文献类型：
--
作者：
Han, Bing;Wang, Yunhao;Gao, Xinbo
行人检测是智能交通系统和高级驾驶员辅助系统的重要组成部分。近年来，行人检测方法已经实现了更高的准确性。然而，现有的算法在实际应用中对于距离摄像机相对较远的小规模行人检测存在不足。在本文中，我们提出了一种新的深度小规模感觉网络（简称SSN）的小规模行人检测。所提出的架构可以产生一些建议的区域，更有效地检测小规模的行人。此外，我们设计了一种新的损失函数的基础上交叉熵损失增加损失的贡献，难以检测的小规模行人。此外，引入了一种新的评价指标，可以衡量行人检测方法的定位精度。此外，一个亚洲行人检测数据集VIP行人数据集是从各种道路条件数据构建的。我们的方法在加州理工学院的行人数据集和我们的VIP行人数据集上取得了良好的检测性能。
Pedestrian detection is a crucial component for intelligent transport system and advanced driver assistance system. In recent years, pedestrian detection methods have achieved higher accuracy. However, the existing algorithms are insufficient for small-scale pedestrian detection that is relatively far from cameras in practical applications. In this paper, we propose a novel deep small-scale sense network (termed SSN) for small-scale pedestrian detection. The proposed architecture could generate some proposal regions which are more effective to detect small-scale pedestrians. Furthermore, we design a novel loss function based on cross entropy loss to increase the loss contribution from hard-to-detect small-scale pedestrians. In addition, a novel evaluation metric is introduced, which can measure the location precision of the pedestrian detection methods. In addition an Asian pedestrian detection dataset named VIP pedestrian dataset is constructed from various road condition data. Our method achieves good detection performance on Caltech pedestrian dataset and our VIP pedestrian dataset.