In this work, we propose a novel node splitting method for regression trees and incorporate it into the regression forest framework. Unlike traditional binary splitting, where the splitting rule is selected from a predefined set of binary splitting rules via trial-and-error, the proposed node splitting method first finds clusters of the training data which at least locally minimize the empirical loss without considering the input space. Then splitting rules which preserve the found clusters as much as possible are determined by casting the problem into a classification problem. Consequently, our new node splitting method enjoys more freedom in choosing the splitting rules, resulting in more efficient tree structures. In addition to the Euclidean target space, we present a variant which can naturally deal with a circular target space by the proper use of circular statistics. We apply the regression forest employing our node splitting to head pose estimation (Euclidean target space) and car direction estimation (circular target space) and demonstrate that the proposed method significantly outperforms state-of-the-art methods (38.5 and 22.5 error reduction respectively).
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. In this paper we propose a framework for learning a regression function form a set of local features in an image. The regression is learned from an embedded representation that reflects the local features and their spatial arrangement as well as enforces supervised manifold constraints on the data. We applied the approach for viewpoint estimation on a Multiview car dataset, a head pose dataset and arm posture dataset. The experimental results show that this approach has superior results (up to 67 improvement) to the state-of-the-art approaches in very challenging datasets.