Actions speak louder than words: Searching human action video based on body movement

Human action video search is a frequent demand in multimedia applications, and conventional video search schemes based on keywords usually fail to correctly find relevant videos due to noisy video tags. Observing the widespread use of Kinect-like depth cameras, we propose to search human action videos by directly performing the target action with body movements. Human actions are captured by Kinect and the recorded depth information is utilized to measure the similarity between the query action and each human action video in the database. We use representative depth descriptors without learning optimization to achieve real-time and promising performance as compatible as those of the leading methods based on color images and videos. Meanwhile, a large Depth-included Human Action video dataset, namely DHA, is collected to prove the effectiveness of the proposed video search system.