A complete semantics-based behavior recognition approach that depends on object tracking has been introduced in this project. The proposed framework obtains 3-D object level information by detecting and tracking people in the scene using a blob matching technique. Based on the temporal properties of these blobs, behaviours and events are semantically recognized by employing object and inter object motion features. A number of types of behavior videos that are relevant to security in public transport areas have been selected to demonstrate the capabilities of this approach. Examples of these are abandoned and stolen objects, fighting, fainting, and loitering. Using some videos, the experimental results presented here demonstrate the performance this approach.
Video surveillance system, Semantic based approach, Behavior classification .