Self-Updatable Database System Based on Human Motion Assessment Framework

Recently, human motion-centric videos have been attracting attention in the field of computer vision. Observing and detecting human motion in intelligent surveillance camera systems is essential for understanding the intentions of target subjects. However, these videos have vast amounts of disparate and complex information, and hence they are difficult to process and label automatically. As a result, building and maintaining a database using motion-centric videos requires considerable labor in trimming and classifying the videos. Therefore, we propose a self-updatable motion database system based on a human motion assessment framework for evaluating complex human movements. The framework quantifies three primitive motion properties: stability, liveliness, and attention. This assessment highlights the semantics of human motion in the input video. The semantic motion sequence obtained after the motion assessment is compared with a similarity motion database to determine whether the database needs to be updated; for efficient comparison, we introduce a sequential autoencoder model with a long short-term memory neural network. The proposed system maintains the database within a surveillance camera system using a motion update algorithm; unseen motions in the database are updated using a camera-based surveillance system. In addition, this framework combines state-of-art action recognition methods to improve performance by up to 11% via the self-update of motion.