Anomaly Detection in Spatiotemporal Neural Networks KU Leuven
In this doctoral study, we study and refine techniques to find anomalies in spatiotemporal image data from various industrial applications. After all, we notice that better detection can be achieved if the frame-by-frame processing of an image sequence is abandoned and the video is processed as a coherent entity by the neural network. This is true for applications of object detection, but certainly for action recognition is the inclusion of ...