Using a 3D CNN for Rejecting False Positives on Pedestrian Detection

Self-driving cars are becoming slowly but surely the future of transport. Nonetheless, in order to achieve fully automatic operation, several challenges are still needed to be tackled. One of the main goals that is currently being pursued is a very accurate scene understanding and object detection. In this regard, the most accurate object detectors are image-based. However, these methods yield critical flaws that make them prone to error in some specific scenarios. For instance, actual objects would be detected in depictions of such objects. The urban environment is strewn with these cases. Namely, in billboards and advertisements. However, most of the self-driving cars feature a lidar that provides 3D perception. This sensor could help to disambiguate the cases mentioned before. In this paper, we combine the accuracy of 2D deep learning object detectors with a 3D Convolutional Neural Network (3D CNN) for rejecting false positives on pedestrian detection. First, the object detector provides all the detected pedestrians in the scene, and then the 3D CNN is in charge of rejecting or verify the detections. Our proposal is tested on two well-known publicly available datasets and provides up to 84% accuracy.