Detection of Abandoned Objects Using Improved Convolutional Neural Network

Video surveillance cameras can be used as a powerful tool for automating the detection of various situations and helping to make appropriate decisions in order to increase the level of security and protection. One of the most important applications of video surveillance systems is the detection of abandoned objects such as abandoned luggage is to prevent dangerous bombings and other cases. In this regard, in this article, a two-stage model based on deep learning has been introduced to detect abandoned objects. The purpose of the first stage is to detect all the objects in the scene and the second stage is to classify the abandoned objects. In the first step, the Gaussian mixture model (GMM) is used to model the background and detect stationary objects. In the second step, a combination of convolutional neural network (CNN) and the AdaBoost algorithm is used to identify the abandoned objects among all the extracted images. Based on the results of the evaluations, the proposed model has a higher accuracy in detecting abandoned objects than the basic methods.