Obstacle Detection for Intelligent Transportation Systems Using Deep Stacked Autoencoder and k -Nearest Neighbor Scheme

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...

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Published in:	IEEE sensors journal Vol. 18; no. 12; pp. 5122 - 5132
Main Authors:	Dairi, Abdelkader, Harrou, Fouzi, Ying Sun, Senouci, Mohamed
Format:	Journal Article
Language:	English
Published:	IEEE 15.06.2018
Subjects:	autonomous vehicles Clustering algorithms deep learning intelligent transportation systems Kernel Machine learning Machine learning algorithms Obstacle detection Partitioning algorithms Roads Sensors
ISSN:	1530-437X, 1558-1748
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Abstract	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.
AbstractList	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.
Author	Ying Sun Dairi, Abdelkader Senouci, Mohamed Harrou, Fouzi
Author_xml	– sequence: 1 givenname: Abdelkader surname: Dairi fullname: Dairi, Abdelkader email: dairi.aek@gmail.com organization: Comput. Sci. Dept., Univ. of Oran 1 Ahmed Ben Bella, Oran, Algeria – sequence: 2 givenname: Fouzi surname: Harrou fullname: Harrou, Fouzi email: fouzi.harrou@kaust.edu.sa organization: Electr. & Math. Sci. & Eng. Div., King Abdullah Univ. of Sci. & Technol., Thuwal, Saudi Arabia – sequence: 3 surname: Ying Sun fullname: Ying Sun organization: Electr. & Math. Sci. & Eng. Div., King Abdullah Univ. of Sci. & Technol., Thuwal, Saudi Arabia – sequence: 4 givenname: Mohamed surname: Senouci fullname: Senouci, Mohamed organization: Comput. Sci. Dept., Univ. of Oran 1 Ahmed Ben Bella, Oran, Algeria
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SubjectTerms	autonomous vehicles Clustering algorithms deep learning intelligent transportation systems Kernel Machine learning Machine learning algorithms Obstacle detection Partitioning algorithms Roads Sensors
Title	Obstacle Detection for Intelligent Transportation Systems Using Deep Stacked Autoencoder and k -Nearest Neighbor Scheme
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