远距离行人检测使用立体和级联的卷积网络分类器

Z. Kira, R. Hadsell, G. Salgian, S. Samarasekera
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引用次数: 12

摘要

在本文中,我们提出了一个用于远距离检测行人的系统,该系统结合了基于立体的检测、使用深度学习的分类和一系列专门的分类器,可以减少误报和计算负载。具体来说,我们使用立体来执行垂直结构的检测,并根据边缘响应进一步过滤。然后设计了一个卷积神经网络来支持行人的分类,同时使用基于外观和立体差异的特征。第二个卷积网络分类器是专门为远程检测使用外观的情况下训练的。我们使用级联方法和多线程进一步加快了分类器的速度。该系统部署在两个机器人上,一个使用180度鱼眼镜头的高分辨率立体对,另一个使用80度FOV镜头。在各种环境中捕获的大型数据集上展示了结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Long-Range Pedestrian Detection using stereo and a cascade of convolutional network classifiers
In this paper, we present a system for detecting pedestrians at long ranges using a combination of stereo-based detection, classification using deep learning, and a cascade of specialized classifiers that can reduce false positives and computational load. Specifically, we use stereo to perform detection of vertical structures which are further filtered based on edge responses. A convolutional neural network was then designed to support the classification of pedestrians using both appearance and stereo disparity-based features. A second convolutional network classifier was trained specifically for the case of long-range detections using appearance only. We further speed up the classifier using a cascade approach and multi-threading. The system was deployed on two robots, one using a high resolution stereo pair with 180 degree fisheye lenses and the other using 80 degree FOV lenses. Results are demonstrated on a large dataset captured in a variety of environments.
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