Action Detection in Crowded Videos Using Masks
Ping Guo, Zhenjiang Miao · 2010
In this paper, we investigate the task of human action detection in crowded videos. Different from action analysis in clean scenes, action detection in crowded environments is difficult due to the cluttered backgrounds, high densities of people and partial occlusions. This paper proposes a method for action detection based on masks. No human segmentation or tracking technique is required. To cope with the cluttered and crowded backgrounds, shape and motion templates are built and the shape templates are used as masks for feature refining. In order to handle the partial occlusion problem, only the moving body parts in each motion are involved in action training. Experiments using our approach are conducted on the CMU dataset with encouraging results.