Temporally Smoothed Anomaly Detection in Continuous Fluids

Detection of anomalies in continuous fluids is an open and interesting problem in computer vision and pattern recognition. The problem has many challenges which mainly derive from the highly-deformable shape of the liquid over time. To address these challenges, the existing literature mainly exploited infrared sensors. However, application of such solutions is not only highly-expensive because of the required hardware, but it is often limited to the detection anomalies in the fluid temperature. In this work, we tackle the problem by considering sensors working in the visible range. We leverage on different visual feature representation which are smoothed over time to compensate the temporal changes that might not be due to the presence of anomalies but to noisy measurements,etc. Such temporally smoothed representations are then exploited to learn a robust "normality" model by means of a One-Class Support Vector Machine. A real-world scenario dataset has been collected to evaluate the proposed solution.