Real-time Person Tracking in High-resolution Panoramic Video for Automated Broadcast Production

For enabling immersive user experiences for interactive TV services and automating camera view selection and framing, knowledge of the location of persons in a scene is essential. We describe an architecture for detecting and tracking persons in high-resolution panoramic video streams, obtained from the Omni Cam, a panoramic camera stitching video streams from 6 HD resolution tiles. We use a CUDA accelerated feature point tracker, a blob detector and a CUDA HOG person detector, which are used for region tracking in each of the tiles before fusing the results for the entire panorama. In this paper we focus on the application of the HOG person detector in real-time and the speedup of the feature point tracker by porting it to NVIDIA's Fermi architecture. Evaluations indicate significant speedup for our feature point tracker implementation, enabling the entire process in a real-time system.