Learning human behaviour patterns in work environments

In this paper, we propose a flexible, human-oriented framework for learning the behaviour pattern of the users in work environments from visual sensors. The knowledge of human behaviour pattern enables the ambient environment to communicate with the user in a seamless way and make anticipatory decisions, from the automation of appliances and personal schedule reminder to the detection of unhealthy habits. Our learning method is general and learns from a set of activity sequences, where the granularity of activities can vary for different applications. Algorithms to extract the activity information from the videos are described. We evaluate our method on video sequences captured in a real office, where the user's daily routine is recorded over a month. The results show that our approach is capable of not only identifying the frequent behaviour of the user, but also the time relations and conditions of the activities.