Abstract. When given a single static picture, humans can not only interpret the instantaneous content captured by the image, but also they are able to infer the chain of dynamic events that are likely to happen in the near future. Similarly, when a human observes a short video, it is easy to decide if the event taking place in the video is normal or unexpected, even if the video depicts a an unfamiliar place for the viewer. This is in contrast with work in surveillance and outlier event detection, where the models rely on thousands of hours of video recorded at a single place in order to identify what constitutes an unusual event. In this work we present a simple method to identify videos with unusual events in a large collection of short video clips. The algorithm is inspired by recent approaches in computer vision that rely on large databases. In this work we show how, relying on large collections of videos, we can retrieve other videos similar to the query to build a simple model of the distribution of expected motions for the query. Consequently, the model can evaluate how unusual is the video as well as make event predictions. We show how a very simple retrieval model is able to provide reliable results.
Recently, in REF relying on large collections of videos, a simple model of the distribution of expected motions is built using trajectories of keypoints for event prediction.
A data-driven approach for event prediction
Computer Vision–ECCV
Computer Science