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Most of the state-of-the-art approaches to human activity recognition in video need an intensive training stage and assume that all of the training examples are labeled and available beforehand. But these assumptions are unrealistic for many applications where we have to deal with streaming videos. In these videos, as new activities are seen, they can be leveraged upon to improve the current activity recognition models. In this work, we develop an incremental activity learning framework that is able to continuously update the activity models and learn new ones as more videos are seen. Our proposed approach leverages upon state-of-the-art machine learning tools, most notably active learning systems. It does not require tedious manual labeling of every incoming example of each activity class. We perform rigorous experiments on challenging human activity datasets, which demonstrate that the incremental activity modeling framework can achieve performance very close to the cases when all examples are available a priori.
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