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Online Vehicle Detection using Deep Neural Networks and Lidar based Preselected Image Patches

Stefan Lange, Fritz Ulbrich, Daniel Goehring— 2016

In this paper we present a vehicle detection system using convolutional neural networks on 2d image data. Since realtime capabilities are crucial for object detection systems running in real-traffic situations, we will show how the calculation time of our algorithm can be significantly reduced by taking advantage of depth information from lidar sensors. One part of this work focusses on useful network topologies and network parameters to increase the classification precision. We will test the presented algorithm on an autonomous car in different real-traffic scenarios with regards to detection accuracy and calculation time and show experimental results.

TitelOnline Vehicle Detection using Deep Neural Networks and Lidar based Preselected Image Patches
VerfasserStefan Lange, Fritz Ulbrich, Daniel Goehring
VerlagIEEE Xplore
ThemaTraining, Laser radar, Vehicles, Sensors, Cameras, Neurons
Datum20160808
KennungDOI: 10.1109/IVS.2016.7535503
Quelle/n
Erschienen inProceedings of the 2016 IEEE Intelligent Vehicles Symposium (IV16).
ArtText
Größe oder Längepp. 954-959
RechteCopyright by IEEE. When citing this work, cite the original link.