RPN+快速提升树:将深度神经网络与传统分类器相结合用于行人检测

Jiaxiang Zhao, Jun Li, Yingdong Ma
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引用次数: 8

摘要

随着人工智能技术的飞速发展,行人检测问题越来越受到人们的关注。本文提出了一种基于深度神经网络和传统分类器相结合的快速鲁棒行人检测方法。具体来说,使用改进的RPN-VGG方法实现区域建议生成和特征提取。该方法旨在提高系统在小目标检测方面的性能。在RPN输出的基础上训练一种新的分类器,快速提升树,以获得最终结果。在加州理工学院行人数据集上的实验表明,该方法的缺失率为8.77%,是目前最先进的基于cnn的检测器中效率最高的。在不考虑算法效率的情况下,通过加入全局归一化和光流特征,可以将检测质量进一步提高到8.25%的缺失率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
RPN+ fast boosted tree: Combining deep neural network with traditional classifier for pedestrian detection
The problem of pedestrian detection receives increasing attention due to the rapid development of artificial intelligence technologies. In this paper, we propose a deep neural network based method which combines with a traditional classifier for fast and robust pedestrian detection. Specifically, region proposals generation and feature extraction are implemented using a modified RPN-VGG method. The proposed method is designed to improve system performance on small objects detection. A new classifier, Fast Boosted Tree, is trained based on RPN outputs to obtain the final results. Experiments on Caltech pedestrian dataset demonstrate that the proposed method achieves 8.77% miss rate and has the best known efficiency with state-of-the-art CNN-based detectors. When algorithm efficiency is not considered, detection quality can be further improved to 8.25% miss rate by adding global normalization and optical flow features.
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