Multi-object events recognition from video sequences using extended finite state machine

Representation and recognition of human activities or other events generated by vehicles in restricted settings such as airports, railway station and parking lots is of great importance in automated surveillance systems. The problem is difficult because: 1) at the lower level, detection of primitive actions is inaccurate due to changes in illumination, noise and resolution of video camera. 2) at the higher level, activities or events in such settings are usually originated by multi-object. It is hard to represent and parse the complex activities and events. In this paper, we describe a new approach for representing and recognizing the events in video sequences, and the approach is able to handle complex multi-agent interactions. Our experimental results on a dataset that consisting of videos took by normal camera and videos took by surveillance camera in laboratory.