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Depth2Action: Exploring Embedded Depth for Large-Scale Action Recognition
[article]
2016
arXiv
pre-print
This paper performs the first investigation into depth for large-scale human action recognition in video where the depth cues are estimated from the videos themselves. We develop a new framework called depth2action and experiment thoroughly into how best to incorporate the depth information. We introduce spatio-temporal depth normalization (STDN) to enforce temporal consistency in our estimated depth sequences. We also propose modified depth motion maps (MDMM) to capture the subtle temporal
arXiv:1608.04339v1
fatcat:eaby7yy5uzfw7huk3ftrk2k7fm