MiG

Online vehicle detection using Haar-like, LBP and HOG feature based image classifiers with stereo vision preselection

Daniel Neumann, Tobias Langner, Fritz Ulbrich , Daniel Göhring, Dorothee Spitta— 2017

Environment sensing is an essential property for autonomous cars. With the help of sensors, nearby objects can be detected and localized. Furthermore, the creation of an accurate model of the surroundings is crucial for highlevel planning. In this paper, we focus on vehicle detection based on stereo camera images. While stereoscopic computer vision is applied to localize objects in the environment, the objects are then identified by image classifiers. We implemented and evaluated several algorithms from image based pattern recognition in our autonomous car framework, using HOG-, LBP-, and Haar-like features. We will present experimental results using real traffic data with focus on classification accuracy and execution times.

TitelOnline vehicle detection using Haar-like, LBP and HOG feature based image classifiers with stereo vision preselection
VerfasserDaniel Neumann, Tobias Langner, Fritz Ulbrich , Daniel Göhring, Dorothee Spitta
VerlagIEEExplore
ThemaFeature extraction, Cameras, Stereo vision, Automobiles, Three-dimensional displays, Sensors, Vehicle detection
Datum201706
Quelle/n
Erschienen inProceedings of the IEEE Intelligent Vehicles Symposium (IV) June 11-14, 2017, Redondo Beach, CA, USA
Größe oder Länge6 pages
RechteCopyright by IEEE. When citing this work, cite the original link.