Estimating Dense Optical Flow of Objects for Autonomous Vehicles

Autonomous vehicles need to be able to perceive both the presence and motion of objects in the surrounding environment to navigate in the real world. In this work, we propose to solve the tasks of identifying objects and estimating the corresponding motion by viewing them as a single unified task known as instance flow. Instance flow provides the pixel-wise instance mask of an object and the dense optical flow within it. To achieve this, we extended the state of the art object detection model to include a dense optical flow estimator. The estimator is used to estimate the optical flow for each region of interest only, instead of the entire image. We tested the approach by carrying out experiments on publicly available datasets for autonomous driving research, VKITTI, KITTI and HD1K. Furthermore, we also introduced a new instance flow quality metric to evaluate the instance flow estimation.