Pseudo-labels for supervised learning on event-based data
In recent years, event-based cameras have seen increased use because it has a high temporal resolution which overcomes motion blurring, high dynamic range which overcomes extreme illumination conditions and low power consumption, making it suitable for platforms such as drones and self-driving cars. While frame-based computer vision is mature due to the large amounts of data and ground truth available, event-based computer vision is still a work in progress as data sets are not as aplenty and ground truth is scarce for tasks such as object detection. In this paper, we suggest a way to overcome the lack of data by introducing a simple method to generate pseudolabels for event-based data, assuming that the corresponding frame-based data is also available. These pseudo-labels can be treated as ground truth when training on supervised learning algorithms, and we show, for the first time, eventbased car detection in a realistic environment at 100 frames per second with an average precision of 40.8% relative to pseudo-ground truth.