Image segmentation for human motion analysis: methods and applications

Human motion analysis is closely connected with the development of computational techniques capable of automatically identify objects represented in image sequences, track and analyse its movement. Feature extraction is generally the first step in the study of human motion in image sequences which is strictly related to human motion modelling [1]. Next step is feature correspondence, where the problem of matching features between two consecutives image frames is addressed. Finally high level processing can be used in several applications of Computer Vision like, for instance, in the recognition of human movements, activities or poses. This work will focus in the study of image segmentation methods and applications for human motion analysis. Image segmentation methods related to human motion need to deal with several challenges such as: dynamic backgrounds, for instance when the camera is in motion; lighting conditions that can change along the image sequences; occlusion problems, when the subject does not remain inside the workspace; or image sequences with more than one subject in the workspace at the same time. It is not easy to develop methods which can deal with all these problems at once, so it is common to make some assumptions, however each day more robust and accurate methods are being developed. A typical method of image segmentation is background subtraction, which involves the calculi of a reference image followed by the subtraction of each frame of the image sequence from the reference and further threshold of the result [2]. The simplest form is using a timeaveraged background image as reference but it requires a training period absent of foreground objects. Other possibility is describing each pixel in the scene by a mixture of Gaussian distributions, where the weight parameters of the mixture represent the time proportions that those colours stay in the scene, so background components will be the ones with the highest