Bounding Box Propagation for Semi-automatic Video Annotation of Nighttime Driving Scenes

Abstract

Ground-truth annotations are a fundamental requirement for the development of computer vision and deep learning algorithms targeting autonomous driving. Available public datasets have for the most part been recorded in urban settings, while scenes showing countryside roads and nighttime driving conditions are underrepresented in current datasets. In this paper, we present a semi-automated approach for bounding box annotation which was developed in the context of nighttime driving videos. In our three-step approach, we (a) generate trajectory proposals through a tracking-by-detection method, (b) extend and verify object trajectories through single object tracking, and (c) propose a pipeline for efficient semiautomatic annotation of object bounding boxes in videos. We evaluate our approach on the CVL dataset, which focuses on nighttime driving conditions on European countryside roads. We demonstrate the improvements achieved by each processing step, and observe an increase of 23% in recall while precision remains almost constant when compared to the initial tracking-by-detection approach.