Provident Vehicle Detection at Night: The PVDN Dataset

• URL: http://arxiv.org/abs/2012.15376v2
• Date: Sat, 23 Jan 2021 22:00:48 GMT
• Title: Provident Vehicle Detection at Night: The PVDN Dataset
• Authors: Lars Ohnemus and Lukas Ewecker and Ebubekir Asan and Stefan Roos and Simon Isele and Jakob Ketterer and Leopold M\"uller and Sascha Saralajew
• Abstract summary: We present a novel dataset containing 59746 grayscale annotated images out of 346 different scenes in a rural environment at night. In these images, all oncoming vehicles, their corresponding light objects (e.g., headlamps), and their respective light reflections (e.g., light reflections on guardrails) are labeled. With that, we are providing the first open-source dataset with comprehensive ground truth data to enable research into new methods of detecting oncoming vehicles.
• Score: 2.8730465903425877
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: For advanced driver assistance systems, it is crucial to have information about oncoming vehicles as early as possible. At night, this task is especially difficult due to poor lighting conditions. For that, during nighttime, every vehicle uses headlamps to improve sight and therefore ensure safe driving. As humans, we intuitively assume oncoming vehicles before the vehicles are actually physically visible by detecting light reflections caused by their headlamps. In this paper, we present a novel dataset containing 59746 annotated grayscale images out of 346 different scenes in a rural environment at night. In these images, all oncoming vehicles, their corresponding light objects (e.g., headlamps), and their respective light reflections (e.g., light reflections on guardrails) are labeled. This is accompanied by an in-depth analysis of the dataset characteristics. With that, we are providing the first open-source dataset with comprehensive ground truth data to enable research into new methods of detecting oncoming vehicles based on the light reflections they cause, long before they are directly visible. We consider this as an essential step to further close the performance gap between current advanced driver assistance systems and human behavior.

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