Automatic Scenario Recognition for Visual-Surveillance by Combining Probabilistic Graphical Approaches

Abstract

The online recognition and indexing of video-surveillance sequence is firstly helpful for video-surveillance operator for an on-line alarm generation by highlighting abnormal situation. The second utility concerns the off-line retrieval of specific behavior from a stored image sequence in order to discover causes of an alarm. This capability becomes naturally more powerful when the monitoring concerns a network of IP-camera over a wide area or the Internet. The scenario recognition also known as activity recognition is an old and still active topic in computer science and several complementary approaches have been proposed by the Computer vision and the Artificial Intelligence communities. A scenario is composed on a set of elementary events linked with temporal constraints. The difficulty of human activity lies in their complexity, their spatial and temporal variability and also the uncertainty existing over the whole interpretation task. The computer vision approaches are generally focused on numerical approach by using probabilistic (Bui et al., 2004) (Hongeng et al., 2000) (Rabiner, 1989) or neural network (Howell & Buxton, 2002) approach in order to deal with uncertainty of the low level vision tasks. On the other hand, the Artificial Intelligence community has proposed more flexible symbolic approaches permitting a high level recognition capability (Tessier, 2003) (Vu et al., 2003) (Dousson & Maigat, 2007). Our main contribution in this work concerns the integration of these two complementary approaches (probabilistic and symbolic) in the global scenario recognition system. HHMs (Hidden Markov Model) are the most popular probabilistic approach in representing dynamic systems. They have been initially used in speech recognition (Rabiner, 1989) and successfully applied over gesture or activity recognition (Starner & Pentland, 1995). An interesting feature of HMM is its time scale invariance enabling activity with various speeds. Other extensions to the basic HMM have also been used such as the Coupled Hidden Markov Models (CHMMs) for modelling human interactions (Oliver et al., 2000), and variable length Markov models (VLMMs) to locally optimize the size of behavior models (Galata et al., 2001). However Bayesian networks have also been widely used in the computer vision community for object, event or scenario recognition. One important advantage of the Bayesian network is its ability to encode both qualitative and quantitative contextual knowledge, and their dependence.