Real-time pose estimation piggybacked on object detection

Roman Juranek (Brno University of Technology, Czech Republic)


We present an object detector coupled with pose estimation directly in a single compact and simple model, where the detector shares extracted image features with the pose estimator. The output of the classification of each candidate window consists of both object score and likelihood map of poses. This extension introduces negligible overhead during detection so that the detector is still capable of real time operation. We evaluated the proposed approach on the problem of vehicle detection. We used existing datasets with viewpoint/pose annotation (WCVP, 3D objects, KITTI). Besides that, we collected a new traffic surveillance dataset COD20K which fills certain gaps of the existing datasets and we make it public.