Monocular car viewpoint estimation with circular regression forests

Determining the viewpoint of traffic participants provides valuable high-level attributes to constrain the interpretation of their movement, and thus allows more specific predictions of alert behavior. We present a monocular object viewpoint estimation approach that is realized by a random regression forest. In particular, we address the circular and continuous structure of the problem for training the decision trees. Our approach builds on a 2D deformable part based object detector. Using detected cars on the KITTI vision benchmark, we demonstrate performance for continuous viewpoint estimation, ground point estimation, and their integration into a high-dimensional particle filtering framework. Besides location and viewpoint of cars, the filter framework considers full monocular egomotion information of the observing platform. This demonstrates the versatility of using only monocular information processing with appropriate machine learning.