Enzweiler, Markus (Daimler AG), Hummel, Matthias (Daimler AG), Pfeiffer, David (Daimler AG), Franke, Uwe (Daimler AG)

Efficient Stixel-Based Object Recognition

Scheduled for presentation during the Poster Session "Poster session VI" (WePO6S), Wednesday, June 6, 2012, 15:10−16:40, Room T1

2012 Intelligent Vehicles Symposium, June 3-7, 2012, Alcalá de Henares, Spain

This information is tentative and subject to change. Compiled on September 30, 2020

Abstract

This paper presents a novel attention mechanism to improve stereo-vision based object recognition systems in terms of recognition performance and computational efficiency at the same time. We utilize the Stixel World, a compact medium-level 3D representation of the local environment, as an early focus-of-attention stage for subsequent system modules. In particular, the search space of computationally expensive pattern classifiers is significantly narrowed down. We explicitly couple the 3D Stixel representation with prior knowledge about the object class of interest, i.e. 3D geometry and symmetry, to precisely focus processing on well-defined local regions that are consistent with the environment model.

Experiments are conducted on large real-world datasets captured from a moving vehicle in urban traffic. In case of vehicle recognition as an experimental testbed, we demonstrate that the proposed Stixel-based attention mechanism significantly reduces false positive rates at constant sensitivity levels by up to a factor of 8 over state-of-the-art. At the same time, computational costs are reduced by more than an order of magnitude.