Karadag, Ozge Oztımur2026-01-242026-01-2420211300-0632https://search.trdizin.gov.tr/tr/yayin/detay/514920https://doi.org/10.3906/elk-2003-124https://hdl.handle.net/20.500.12868/4376Human 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 recognitioneninfo:eu-repo/semantics/openAccessBilgisayar BilimleriYazılım MühendisliğiArticle10.3906/elk-2003-124292717729514920