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On-line, Incremental Learning for Real-Time Vision Based Movement Recognition

In this paper we tackle the problem of recognising movement classes in real-time surveillance video. We use a popular public dataset, the CAVIAR dataset, which contains ground truth labeling of people and their activities within a shopping centre environment. The task of movement classification is often performed using simple heuristic rules, and performance can suffer when an increased number of rules are added for the task. We provide a formal knowledge maintenance technique, known as Ripple Down Rules, to provide an elegant method of representing and updating the rules. Ripple Down Rules are an on-line, incremental learning strategy, and are highly suitable for this task due to their ability to incorporate new knowledge while maintaining past knowledge.