Observing human activities using movement modelling

In this paper we propose an unsupervised method based on a scene observation approach to human activity detection from video sequences. The approach adopted has two stages: modelling the categories of movements in the scene while the second stage consists of detecting new activities. Human activity is decided based on analysing movement in the scene. Both optical flow, estimated from pairs or frames, and medium term tracking are considered for representing the movement. Statistical modelling using mixtures of Gaussians, with each component representing a specific movement is considered. The tracking approach considers several consecutive frames and uses streaklines for modelling the corresponding movement. During the first stage, a dictionary of activities, characteristic to the observed scene, is formed. New activities are identified using Kullback-Leibler (KL) divergence between any new distribution of motion vectors and the existing ones from the dictionary. The proposed methodology is applied on various video sequences representing both indoor and outdoor scenes.