Online recognition and segmentation for time-series motion with HMM and conceptual relation of actions

In this paper, we propose a robust online action recognition algorithm with a segmentation scheme that detects start and end points of action occurrences. In other words, the algorithm estimates reliably what kind of actions occurring at present time. The algorithm has following characteristics: 1) The algorithm incorporates human knowledge about relation between action names in order to simplify and toughen the algorithm, thus our algorithm can label robustly multiple action names at the same time. 2) The algorithm uses time-series action probability that represents the likelihood of each action occurrence at every frame time. 3) The classification technique with hidden Markov models (HMMs) enables the algorithm to detect robustly and immediately the segmental points. The experimental results using real motion capture data show that our algorithm not only decreases effectively the latency for detecting the segmental points but also prevents the system from making unnecessary segments due to the error of time-series action probability.