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.

Titel
Online Vehicle Detection using Deep Neural Networks and Lidar based Preselected Image Patches
Verfasser
Stefan Lange, Fritz Ulbrich, Daniel Goehring
Verlag
IEEE Xplore
Schlagwörter
Training, Laser radar, Vehicles, Sensors, Cameras, Neurons
Datum
2016-08-08
Kennung
DOI: 10.1109/IVS.2016.7535503
Quelle/n
Erschienen in
Proceedings of the 2016 IEEE Intelligent Vehicles Symposium (IV16).
Art
Text
Größe oder Länge
pp. 954-959
Rechte
Copyright by IEEE. When citing this work, cite the original link.