2015 Volume 7 Pages 18-30
We propose a human lower body pose estimation method for team sport videos, which is integrated with tracking-by-detection technique. The proposed Label-Grid classifier uses the grid histogram feature of the tracked window from the tracker and estimates the lower body joint position of a specific joint as the class label of the multi-class classifiers, whose classes correspond to the candidate joint positions on the grid. By learning various types of player poses and scales of Histogram-of-Oriented Gradients features within one team sport, our method can estimate poses even if the players are motion-blurred and low-resolution images without requiring a motion-model regression or part-based model, which are popular vision-based human pose estimation techniques. Moreover, our method can estimate poses with part-occlusions and non-upright side poses, which part-detector-based methods find it difficult to estimate with only one model. Experimental results show the advantage of our method for side running poses and non-walking poses. The results also show the robustness of our method for a large variety of poses and scales in team sports videos.