Obstacle Detection for Intelligent Transportation Systems Using Deep Stacked Autoencoder and k-Nearest Neighbor Scheme
Obstacle Detection for Intelligent Transportation Systems Using Deep Stacked Autoencoder and k-Nearest Neighbor Scheme
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DOI：
10.1109/jsen.2018.2831082
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发表时间：
2018-06-15
影响因子：
4.3
通讯作者：
Senouci, Mohamed
中科院分区：
文献类型：
--
作者：
Dairi, Abdelkader;Harrou, Fouzi;Senouci, Mohamed
障碍物检测是智能交通系统发展的重要组成部分，可以避免事故的发生。本文提出了一种基于立体视觉的城市障碍物检测方法。该方法使用深度堆叠自动编码器(DSA)模型，该模型结合了贪婪学习特征和降维能力，并使用无监督k近邻(KNN)算法准确可靠地检测到障碍物的存在。我们把障碍物检测看作是一个异常检测问题。我们使用了三个公开可用的数据集，马拉加立体视觉城市数据集，戴姆勒城市分割数据集和班霍夫数据集，对所提出的方法进行了评估。此外，我们还比较了DSA-KNN方法与基于深度信任网络的聚类方案的效率。结果表明，DSA-KNN适合于城市场景的视觉监控。
Obstacle detection is an essential element for the development of intelligent transportation systems so that accidents can be avoided. In this paper, we propose a stereovision-based method for detecting obstacles in urban environment. The proposed method uses a deep stacked auto-encoders (DSA) model that combines the greedy learning features with the dimensionality reduction capacity and employs an unsupervised k-nearest neighbors (KNN) algorithm to accurately and reliably detect the presence of obstacles. We consider obstacle detection as an anomaly detection problem. We evaluated the proposed method by using practical data from three publicly available data sets, the Malaga stereovision urban data set, the Daimler urban segmentation data set, and the Bahnhof data set. Also, we compared the efficiency of DSA-KNN approach to the deep belief network-based clustering schemes. Results show that the DSA-KNN is suitable to visually monitor urban scenes.