Detection and identification of unattended/removed objects in video surveillance

This paper presents an unattended or removed Object detection method for video surveillance data captured by single static camera. Two backgrounds are modelled with different learning rates; one for transient and another for permanent. These both backgrounds are defined as a mixture of Gaussian model, it uses online Bayesian for updating. Two binary foregrounds are extracted by subtracting those backgrounds from current frame. Then shadow removing algorithms are applied to extract the real shape of the foreground objects. Then blob level likelihood image model is used to detect temporary static objects. Then we classify the extracted object using features like size, height, width, colour of that object. Input to this is video and the output will be detection and classification of abandoned objects. Finally, we trigger an alarm on detection of abandoned object. We provide good results and efficient model which can be used in real time surveillance systems.