rCRF: Recursive Belief Estimation over CRFs in RGB-D Activity Videos
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For assistive robots, anticipating the future actions of humans is an essential task. This requires modeling both the evolution of the activities over time and the rich relationships between humans and the objects. Since the future activities of humans are quite ambiguous, robots need to assess all the future possibilities in order to choose an appropriate action. Therefore, a successful anticipation algorithm needs to compute all plausible future activities and their corresponding probabilities. In this paper, we address the problem of efficiently computing beliefs over future human activities from RGB-D videos. We present a new recursive algorithm that we call Recursive Conditional Random Field (rCRF) which can compute an accurate belief over a temporal CRF model. We use the rich modeling power of CRFs and describe a computationally tractable inference algorithm based on Bayesian filtering and structured diversity. In our experiments, we show that incorporating belief, computed via our approach, significantly outperforms the state-of-the-art methods, both in terms of accuracy and computation time.
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