|Topic:||Odhadování optického toku pro autonomní řízení|
|Supervisor:||Mgr. Jan Šochman, Ph.D.|
|Announce as:||Diplomová práce, Bakalářská práce, Semestrální projekt|
|Description:||We are part of the Toyota research lab, a research project at CTU sponsored by Toyota, and our goal is to understand dynamic scenes from a video recording, to know what moves and what is static, and where the moving objects move to. Our main application is a self-driving car equipped with a camera, but many other applications exist in robotics, human-computer interfaces, movie editing, ... Observing the scene through a single camera, however, one observes only a 2D projection of the actual 3D movement. We call this projection optical flow (OF) and it is represented as a 2D vector for every pixel in the frame.
The task of OF estimation has its roots in 80's and has been recently (as many other computer vision problems) dominated by solutions based on convolutional neural networks (CNNs). We have developed several algorithms in recent years ourselves, usually with world class performance. Yet, the problem is still not solved sufficiently well and even the best methods fail surprisingly often.
The exact problem formulation will be specified depending on the current needs of the project and the student experience (Bc./Ing.).
A possible currently interesting problem is to speed up existing OF method to (close to) real-time performance. This would be beneficial for various applications or for training other methods which rely on the OF output.
The student has to be able to code in Python and some knowledge of deep learning frameworks like PyTorch or Tensorflow is advantageous but not necessary.
|Bibliography:|| Continual Occlusions and Optical Flow Estimation: https://arxiv.org/abs/1811.01602
 PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume: https://arxiv.org/abs/1709.02371
 KITTI benchmark: http://www.cvlibs.net/datasets/kitti/eval_scene_flow.php?benchmark=flow