Autonomous Visual Events Detection and Classification without Explicit Object-Centred Segmentation and Tracking

Modelling events is one of the key problems in dynamic scene analysis when salient and autonomous visual changes occuring in a scene need to be characterised effectively as meaningful events. We propose a new approach for modelling such temporal events based on the local intensity temporal history of pixels. The method provides a computationally very effective temporal measure for detecting autonomous events. Events are represented and detected first at the pixel level and then at a blob level (grouped pixels) autonomously. The Expectation-Maximisation (EM) algorithm is employed to cluster events with automatic model order selection using modified Minimum Description Length (MDL). Experiments are presented to demonstrate that meaningful clusters of blob-level events can be formed without object segmentation and tracking.