Blur Detection for Surveillance Camera System

Surveillance cameras, which are often placed in unconstrained environments, can be tampered with due to many environmental and human factors. It results in degraded surveillance videos and affects the subsequent smart applications that make use of the videos in decision-making. Blur anomaly is one of the most typical problems in those videos, which have the target objects in the videos significantly blurred or partially occluded. Such blur anomalies should be detected as soon as possible to ensure the integrity of the video data. In this study, a novel self-supervised blur detection model is proposed. The model focuses on the detection of four commonly found blur anomalies in surveillance videos. They are the natural blur, defocus blur, dirt blur, and spray-paint blur. By using the self-supervised learning method, we can fully make use of the abundant positive samples to improve detection accuracy. Since, in many situations, camera anomaly detection needs to be carried out with edge devices, we also propose a compressed residual network, which incorporates a color attention module, to reduce the model complexity so that it can be applied to edge applications. Our experimental results show that the proposed model significantly improves over the existing approaches in terms of complexity and accuracy.