A system of automated training sample generation for visual-based car detection

Abstract

This paper presents a system to automatically generate car sample dataset for visual-based car detector training. The dataset contains multi-view car samples labeled with the car's pose, so that a view-discriminative training and car detection is also available. There are mainly two parts in the system: laser-based car detection and tracking generates motion trajectories of on-road cars, and then visual samples are extracted by fusing the detection and tracking results with visual-based detection. A multi-modal sensor system is developed for the omni-directional data collection on a test-bed vehicle. By processing the data of experiment conducted on the freeway of Beijing, a large number of multi-view car samples with pose information were generated. The samples' quality is evaluated by applying it in a visual car detector's training and testing procedure.