Abstract
This paper presents Space-Time Occupancy Patterns (STOP), a new visual representation for 3D action recognition from sequences of depth maps. In this new representation, space and time axes are divided into multiple segments to define a 4D grid for each depth map sequence. The advantage of STOP is that it preserves spatial and temporal contextual information between space-time cells while being flexible enough to accommodate intra-action variations. Our visual representation is validated with experiments on a public 3D human action dataset. For the challenging cross-subject test, we significantly improved the recognition accuracy from the previously reported 74.7% to 84.8%. Furthermore, we present an automatic segmentation and time alignment method for online recognition of depth sequences.
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Vieira, A.W., Nascimento, E.R., Oliveira, G.L., Liu, Z., Campos, M.F.M. (2012). STOP: Space-Time Occupancy Patterns for 3D Action Recognition from Depth Map Sequences. In: Alvarez, L., Mejail, M., Gomez, L., Jacobo, J. (eds) Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications. CIARP 2012. Lecture Notes in Computer Science, vol 7441. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-33275-3_31
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