| Yazarlar (2) |
Senem Tanberk
|
|
Onur Demir
|
| Özet |
| Temporal video action segmentation (TVAS) is a challenging problem in analyzing and understanding long-term video content. Action segmentation attracts significant interest from both researchers and practitioners to many applications such as video editing in social media, surveillance in security, robotics, daily activities in instructional videos, and sports analysis. This paper addresses the action segmentation problem in videos including procedural activities as a supervised approach. We proposed an end-to-end framework to segment video streams by applying the sliding window method and state-of-the-art deep models. First, proposed pipeline extracts spatiotemporal features from the input video. Second, with the supervised approach with the sliding window method, we applied domain-specific segmentation task which belongs to the text segmentation domain to video domain. Third, we performed action … |
| Anahtar Kelimeler |
| Bildiri Türü | Tebliğ/Bildiri |
| Bildiri Alt Türü | Tam Metin Olarak Yayınlanan Tebliğ (Uluslararası Kongre/Sempozyum) |
| Bildiri Niteliği | Alanında Hakemli Uluslararası Kongre/Sempozyum |
| Bildiri Dili | İngilizce |
| Kongre Adı | 2023 8th International Conference on Computer Science and Engineering (UBMK) |
| Kongre Tarihi | 13-09-2023 / 13-09-2023 |
| Basıldığı Ülke | Türkiye |
| Basıldığı Şehir |
| Atıf Sayıları | |
| Google Scholar | 1 |
