An adversarial framework for open-set human action recognition using skeleton data
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Tarih
2021
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Human action recognition is a fundamental problem which is applied in various domains, and it is widely\rstudied in the literature. Majority of the studies model action recognition as a closed-set problem. However, in real-\rlife applications it usually arises as an open-set problem where a set of actions are not available during training but\rare introduced to the system during testing. In this study, we propose an open-set action recognition system, human\raction recognition and novel action detection system (HARNAD), which consists of two stages and uses only 3D skeleton\rinformation. In the first stage, HARNAD recognizes a given action and in the second stage it decides whether the\raction really belongs to one of the a priori known classes or if it is a novel action. We evaluate the performance of the\rsystem experimentally both in terms of recognition and novelty detection. We also compare the system performance with\rstate-of-the-art open-set recognition methods. Our experiments show that HARNAD is compatible with state-of-the-art\rmethods in novelty detection, while it is superior to those methods in recognition
Açıklama
Anahtar Kelimeler
Bilgisayar Bilimleri, Yazılım Mühendisliği
Kaynak
Turkish Journal of Electrical Engineering and Computer Sciences
WoS Q Değeri
Scopus Q Değeri
Cilt
29
Sayı
2
