Titlebook: Gesture Recognition; Sergio Escalera,Isabelle Guyon,Vassilis Athitsos Book 2017 Springer International Publishing AG 2017 Artificial intel

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L. F. Liu,R. H. Li,X. J. Yang,J. Z. Renfer from “noise” such as mislabeling, or inaccurate identification of start and end time of gesture instances. In this paper we present SegmentedLCSS and WarpingLCSS, two template-matching methods offering robustness when trained with noisy crowdsourced annotations to spot gestures from wearable mot

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Trevor L. L. Orr PhD,Eric R. Farrell PhDion domains for this type of technology. As in many other computer vision areas, deep learning based methods have quickly become a reference methodology for obtaining state-of-the-art performance in both tasks. This chapter is a survey of current deep learning based methodologies for action and gest

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Human Gesture Recognition on Product Manifolds,e geometry of tensor space is often ignored. The aim of this paper is to demonstrate the importance of the intrinsic geometry of tensor space which yields a very discriminating structure for action recognition. We characterize data tensors as points on a product manifold and model it statistically u

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Sign Language Recognition Using Sub-units, appearance data as well as those inferred from both 2D or 3D tracking data. These sub-units are then combined using a sign level classifier; here, two options are presented. The first uses Markov Models to encode the temporal changes between sub-units. The second makes use of Sequential Pattern Boo

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Language-Motivated Approaches to Action Recognition,sight into the underlying patterns of motions in activities, we develop a dynamic, hierarchical Bayesian model which connects low-level visual features in videos with poses, motion patterns and classes of activities. This process is somewhat analogous to the method of detecting topics or categories

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