Combining Visual Saliency Methods and Sparse Keypoint Annotations to Create Object Representations for Providently Detecting Vehicles at Night

Provident detection of other road users at night has the potential for increasing road safety. For this purpose, humans intuitively use visual cues, such as light cones and light reflections emitted by other road users to be able to react to oncoming traffic at an early stage. Computer vision methods can imitate this behavior by predicting the appearance of vehicles based on light reflections caused by the vehicle’s headlights. Since current object detection algorithms are mainly based on detecting directly visible objects annotated via bounding boxes, the detection and annotation of light reflections without sharp boundaries is challenging. For this reason, the extensive open-source PVDN (Provident Vehicle Detection at Night) dataset was published that includes traffic scenarios at night with light reflections annotated via keypoints. In this paper, we explore a generic approach to annotate objects without clear boundaries, such as light reflections, by combining sparse keypoint annotations of humans with the concept of Boolean map saliency. With that, we create context-aware saliency maps that capture unsharp object boundaries, such as of light reflections. We show that this approach allows for an automated derivation of different object representations, such as bounding boxes, so that detection models can be trained and the problem of providently detecting vehicles at night can be tackled from a different perspective. Our approach makes it possible to derive bounding boxes with superior quality compared to previous approaches and to develop better object detection algorithms. With this paper, we provide a powerful method to study the problem of detecting objects with unsharp boundaries and, in particular, to investigate the detection of vehicles at night before they are actually visible.