SILHOUETTE BASED HUMAN ACTION RECOGNITION

Abstract

Human motion analysis is currently receiving increasing attention from computer vision researchers. This interest is motivated by applications over a wide spectrum of topics. For example, segmenting the parts of the human body in an image, tracking the movement of joints over an image sequence, and recovering the underlying 3D body structure are particularly useful for analysis of athletic performance, as well as medical diagnostics. The capability to automatically monitor human activities using computers in security-sensitive areas such as airports, border crossings, and building lobbies is of great interest to the police and military. With the development of digital libraries, the ability to automatically interpret video sequences will save tremendous human effort in sorting and retrieving images or video sequences using content-based queries. Other applications include building man-machine user interfaces and video conferencing. The research trend in the field of action recognition has recently led to more robust techniques, which to some extent are applicable for action recognition in complex scenes. Action recognition in complex scenes is an extremely difficult task due to challenges such as background clutter, camera motion, occlusions and illumination variations. To address these challenges, several methods, like tree-based template matching, tensor canonical correlation, prototype based action matching, incremental discriminant analysis of canonical correlation, latent pose estimation and a generalised Hough transform were proposed. Most of these methods are very complex and require pre-processing, like segmentation, tree data structure building, target tracking, background subtraction or the fitting of a human body model. On the other hand, recently, spatio-temporal features have gained popularity because of their state-of-theart performance with reduced or even no pre-processing. These methods apply interest point detectors and local descriptors to characterize and encode the video data, and thereby perform action classification.