Real-time detection of abandoned bags using CNN

The problem of automatic abandoned bag detection is of the great importance for ensuring security in the public areas. At the same time emergency situations occur rarely in the large-scale video surveillance systems. Therefore it is important to keep false alarms low maintaining high accuracy of detection. The approach that satisfies mentioned requirements for abandoned bag detection in complex environments is proposed. It consists of two blocks. The first block does the preliminary detection of abandoned bags on pixel level by background modelling via Gaussian mixture model. It ensures high speed and precise positioning of the bounding boxes on the objects of interest. The second part performs the bag recognition on a region level via a compact convolutional neural network. Using of the convolutional neural network is a key component to success. All processing happens on a central processing unit. The proposed approach is suitable for systems (microcomputers), which do not have powerful graphical subsystems. The experiments have been conducted on the real-world scenes. Obtained results indicate that the proposed approach is efficient and provides acceptable quality characteristics.