Abstract
Human Action Recognition (HAR) has many applications in surveillance, gaming, animation and Active and Assisted Living (AAL). Several actions performed in daily life are composed of various poses arranged sequentially in time. Recognition of such actions is a difficult and challenging task. The classification approach proposed in this paper considers an analogy between actions and text, where an action is considered as a sentence and a single pose as a word. In the first stage, the poses are grouped based on their similarity and are then assigned labels. These labels are used for constructing label sequences representing motion. We propose Hierarchical Agglomerative Clustering (HAC) for clustering poses. Once the actions are modelled as the spatio-temporal evolution of key poses, we classify the actions using the Hidden Markov Model (HMM) and Hyper-dimensional Computing (HDC) classifiers. The experiments are performed on different datasets using both classifiers and the results are indicative of the effectiveness of the proposed approach in comparison with state-of-the-art methods.
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Tyagi, A., Patel, A., Shah, P. (2020). Text Like Classification of Skeletal Sequences for Human Action Recognition. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12047. Springer, Cham. https://doi.org/10.1007/978-3-030-41299-9_26
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DOI: https://doi.org/10.1007/978-3-030-41299-9_26
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