Environment adaptive pedestrian detection using in-vehicle camera and GPS
Environment adaptive pedestrian detection using in-vehicle camera and GPS
复制标题
DOI：
10.5220/0004677003540361
复制
发表时间：
2015-10
期刊：
影响因子：
--
通讯作者：
Daichi Suzuo;Daisuke Deguchi;I. Ide;H. Murase;H. Ishida;Y. Kojima
中科院分区：
文献类型：
--
作者：
Daichi Suzuo;Daisuke Deguchi;I. Ide;H. Murase;H. Ishida;Y. Kojima
近年来，从车载摄像头图像中准确检测行人是开发安全驾驶辅助系统的重点。目前，成功的方法是基于统计学习。然而，在这样的方法中，需要准备大量的训练图像。因此，训练图像数量的减少降低了检测精度。也就是说，在训练图像很少或没有训练图像的驾驶环境中，难以准确地检测行人。因此，我们提出了一种方法，自动收集训练图像，以建立各种驾驶环境的分类器。这期望通过使用与当前位置相对应的适当分类器来实现高精度的行人检测。该方法由三个步骤组成;驾驶场景的分类，收集非行人图像和训练分类器为每个场景类，并将场景类特定的分类器与GPS位置信息相关联。通过实验，我们证实了该方法的有效性相比，基线方法。
In recent years, accurate pedestrian detection from in-vehicle camera images is focused to develop a safety driving assistance system. Currently, successful methods are based on statistical learning. However, in such methods, it is necessary to prepare a large amount of training images. Thus, the decrease in the number of training images degrades the detection accuracy. That is, in driving environments with few or no training images, it is difficult to detect pedestrians accurately. Therefore, we propose an approach that collects training images automatically to build classifiers for various driving environments. This is expected to realize highly accurate pedestrian detection by using an appropriate classifier corresponding to the current location. The proposed method consists of three steps; Classification of driving scenes, collection of non-pedestrian images and training of classifiers for each scene class, and associating a scene-class-specific classifier with GPS location information. Through experiments, we confirmed the effectiveness of the method compared to baseline methods.