Automatic activity estimation based on object behavior signature

Automatic estimation of human activities is a topic widely studied. However the process becomes difficult when we want to estimate activities from a video stream, because human activities are dynamic and complex. Furthermore, we have to take into account the amount of information that images provide, since it makes the modelling and estimation activities a hard work. In this paper we propose a method for activity estimation based on object behavior. Objects are located in a delimited observation area and their handling is recorded with a video camera. Activity estimation can be done automatically by analyzing the video sequences. The proposed method is called "signature recognition" because it considers a space-time signature of the behaviour of objects that are used in particular activities (e. g. patients' care in a healthcare environment for elder people with restricted mobility). A pulse is produced when an object appears in or disappears of the observation area. This means there is a change from zero to one or vice versa. These changes are produced by the identification of the objects with a bank of nonlinear correlation filters. Each object is processed independently and produces its own pulses; hence we are able to recognize several objects with different patterns at the same time. The method is applied to estimate three healthcare-related activities of elder people with restricted mobility