Hunting Nessie -- Real-Time Abnormality Detection from Webcams
We present a data-driven, unsupervised method for unusual scene detection from static webcams. Such time-lapse data is usually captured with very low or varying framerate. This precludes the use of tools typically used in surveillance (e.g., object tracking). Hence, our algorithm is based on simple image features. We define usual scenes based on the concept of meaningful nearest neighbours instead of building explicit models. To effectively compare the observations, our algorithm adapts the data representation. Furthermore, we use incremental learning techniques to adapt to changes in the data-stream. Experiments on several months of webcam data show that our approach detects plausible unusual scenes, which have not been observed in the data-stream before.