Unsupervised Event Detection in Videos

This paper presents a method for automatic event detection in videos. The method requires no prior definition of events or their modeling and is thus suitable for unsupervised learning of events in a variety of applications. The presented approach relies on firing patterns of a collection of random local detectors in the field of view of a camera. The firing patterns of the detectors are clustered through a modified vector quantization approach. The resulting clusters are annotated, if needed to assign a meaningful description to each clustered group of firing patterns. The resulting codebook can be used for future detection of events of interests. We demonstrate our approach using data from two different applications.