Detection of inconsistent regions in video streams

Humans have a general understanding about their environment. We possess a sense of distinction between what is consistent and inconsistent about the environment based on our prior experience. Any aspect of the scene that does not fit into this definition of normalcy tends to be classified as an inconsistent event, also referred to as novel event. An example of this is a casual observer standing over a bridge on a freeway, tracking vehicle traffic, where the vehicles traveling at or around the same speed limit are generally ignored and a vehicle traveling at a much higher (or lower) speed is subject to one's immediate attention. In this paper, we present a computational learning based framework for novelty detection on video sequences. The framework extracts low-level features from scenes, based on the focus of attention theory and combines unsupervised learning with habituation theory for learning these features. The paper presents results from our experiments on natural video streams for identifying novelty in velocity of moving objects and static changes in the scene.