DeepAnomaly: Combining Background Subtraction and Deep Learning for Detecting Obstacles …

Open AccessArticle Sensors 2016, 16(11), 1904; doi:10.3390/s16111904 (registering DOI) 1 Department of Engineering, Aarhus University, Aarhus 8200, Denmark 2 Danske Commodities, Aarhus 8000, Denmark 3 AgroIntelli, Aarhus 8200, Denmark * Author to whom correspondence should be addressed. Academic Editors: Gabriel Oliver-Codina, Nuno Gracias and Antonio M. López Received: 15 September 2016 / Revised: 26 October 2016 / Accepted: 7 November 2016 / Published: 11 November 2016 Convolutional neural network (CNN)-based systems are increasingly used in autonomous vehicles for detecting obstacles. CNN-based object detection and per-pixel classification (semantic segmentation) algorithms are trained for detecting and classifying a predefined set of object types. These algorithms have difficulties in detecting distant and heavily occluded objects and are, by definition, not capable of detecting unknown object types or unusual scenarios. The visual characteristics of…


Link to Full Article: DeepAnomaly: Combining Background Subtraction and Deep Learning for Detecting Obstacles …

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