Head and upper body pose estimation in team sport videos

We propose a head and upper body pose estimation method in low-resolution team sports videos such as for American Football or Hockey, where all players wear helmets and often lean forward. Compared to the pedestrian cases in surveillance videos, head pose estimation technique for team sports videos has to deal with various types of activities (poses) and image scales according to the position of the player in the field. Using both the pelvis aligned player tracker and the head tracker, our system tracks the player's pelvis and head positions, which results in estimation of player's 2D spine. Then, we estimate the head and upper body orientations independently with random decision forest classifiers learned from a dataset including multiple-scale images. Integrating upper body direction and 2D spine pose, we also estimate the 3D spine pose of the player. Experiments show our method can estimate head and upper body pose accurately for sports players with intensive movement even without any temporal filtering techniques by focusing on the upper body region.