A framework for detecting complex events in surveillance videos

This paper presents a framework for detecting complex events in surveillance videos. Moving objects in the foreground are detected in the object detection component of the system. Whether these foregrounds are human or not is decided in the object recognition component. Then each detected object is tracked and labeled in the object tracking component, in which true labeling of objects in the occlusion situation is also provided. The extracted information is fed to the event detection component. Rule based event models are created and trained using Markov Logic Networks (MLNs) so that each rule is given a weight. Events are inferred using MLNs where the assigned weights are used to determine whether an event occurs or not. The proposed system can be applied to detect many complex events simultaneously. In this paper, detection of left object event is discussed and evaluated using PETS-2006, CANTATA and our dataset.