Online vehicle detection using Haar-like, LBP and HOG feature based image classifiers with stereo vision preselection
Daniel Neumann, Tobias Langner, Fritz Ulbrich, Dorothee Spitta, Daniel Goehring · 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 high-level 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.