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Learning discriminative trajectorylet detector sets for accurate skeleton-based action recognition
[article]
2015
arXiv
pre-print
The introduction of low-cost RGB-D sensors has promoted the research in skeleton-based human action recognition. Devising a representation suitable for characterising actions on the basis of noisy skeleton sequences remains a challenge, however. We here provide two insights into this challenge. First, we show that the discriminative information of a skeleton sequence usually resides in a short temporal interval and we propose a simple-but-effective local descriptor called trajectorylet to
arXiv:1504.04923v1
fatcat:wbcwjpedejgcrgdkrx5dklqtae