Real-time pose estimation piggybacked on object detection
(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.
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.