Task 2777.010-Task 2777.011: Learning Event-based Height from Plane and Parallax

Abstract: Event-based cameras are a novel asynchronous sensing modality that provides exciting benefits, such as the ability to track fast moving objects with no motion blur and low latency, high dynamic range, and low power consumption. Given the low latency of the cameras, as well as their ability to work in challenging lighting conditions, these cam- eras are a natural fit for reactive problems such as fast obstacle detection. In this work, we propose a fast method to perform obstacle detection for vehicles traveling in a roughly 2D environment (e.g. in an environment with a ground plane). Our method transfers the method of plane and parallax to events, which, given the homography to a ground plane and the pose of the camera, generates a warping of the events which removes the optical flow for events on the ground plane, while inducing flow for events above the ground plane. We then estimate dense flow in this warped space using a weakly supervised neural network, which provides the height of each obstacle in the scene. We evaluate our method on the Multi Vehicle Stereo Event Camera dataset, and show its ability to rapidly detect a number of objects both at high speeds and in low lighting conditions.

Bio: Kenneth Chaney is a PhD student at the University of Pennsylvania, working with Dr. Kostas Daniilidis. His current research explores novel algorithms for event cameras for computer vision, as well as unsupervised deep learning methods for motion estimation. He received his Bachelors from Drexel University.