A Reactive Deep Learning-Based Model for Quality Assessment in Airport Video Surveillance Systems

Monitoring the correct operation of airport video surveillance systems is of great importance in terms of the image quality provided by the cameras. Performing this task using human resources is time-consuming and usually associated with a delay in diagnosis. For this reason, in this article, an automatic system for image quality assessment (IQA) in airport surveillance systems using deep learning techniques is presented. The proposed method monitors the video surveillance system based on the two goals of "quality assessment" and "anomaly detection in images". This model uses a 3D convolutional neural network (CNN) for detecting anomalies such as jitter, occlusion, and malfunction in frame sequences. Also, the feature maps of this 3D CNN are concatenated with feature maps of a separate 2D CNN for image quality assessment. This combination can be useful in improving the concurrence of correlation coefficients for IQA. The performance of the proposed model was evaluated both in terms of quality assessment and anomaly detection. The results show that the proposed 3D CNN model could correctly detect anomalies in surveillance videos with an average accuracy of 96.48% which is at least 3.39% higher than the compared methods. Also, the proposed hybrid CNN model could assess image quality with an average correlation of 0.9014, which proves the efficiency of the proposed method.