Hierarchical Model-Based Activity Recognition With Automatic Low-Level State Discovery

Abstract

Activity recognition in video streams is increasingly important for both the computer vision and artificial intelligence communities. Activity recognition has many applications in security and video surveillance. Ultimately in such applications one wishes to recognize complex activities, which can be viewed as combination of simple activities. In this paper, we present a general framework of a Dlevel dynamic Bayesian network to perform complex activity recognition. The levels of the network are constrained to enforce state hierarchy while the Dth level models the duration of simplest event. Moreover, in this paper we propose to use the deterministic annealing clustering method to automatically define the simple activities, which corresponds to the low level states of observable levels in a Dynamic Bayesian Networks. We used real data sets for experiments. The experimental results show the effectiveness of our proposed method.