Masks based human action detection in crowded videos

This paper discusses the task of human action detection in crowded videos. First, we propose a novel mask based shape matching method for action recognition. Our method does not need human detection or segmentation, and it can be used in both clean and crowed backgrounds. Next, shape and flow based features are combined due to their complementary nature. For each action, a binary sequence is used as the template for both shape and flow matching. For a testing sequence and a template sequence, dynamic time warping technique is first applied for time alignment, then shape and flow matching distances are computed between matched frames. We test our algorithm on the CMU dataset and achieve an encouraging performance.