Temporal Hierarchical Dictionary Guided Decoding for Online Gesture Segmentation and Recognition
Online segmentation and recognition of skeleton- based gestures are challenging. Compared with offline cases, the inference of online settings can only rely on the current few frames and always completes before whole temporal movements are performed. However, incompletely performed gestures are ambi...
| Veröffentlicht in: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. - 1992. - 29(2020) vom: 09., Seite 9689-9702 |
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| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2020
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| Zugriff auf das übergeordnete Werk: | IEEE transactions on image processing : a publication of the IEEE Signal Processing Society |
| Schlagworte: | Journal Article |
| Zusammenfassung: | Online segmentation and recognition of skeleton- based gestures are challenging. Compared with offline cases, the inference of online settings can only rely on the current few frames and always completes before whole temporal movements are performed. However, incompletely performed gestures are ambiguous and their early recognition is easy to fall into local optimum. In this work, we address the problem with a temporal hierarchical dictionary to guide the hidden Markov model (HMM) decoding procedure. The intuition is that, gestures are ambiguous with high uncertainty at early performing phases, and only become discriminate after certain phases. This uncertainty naturally can be measured by entropy. Thus, we propose a measurement called "relative entropy map" (REM) to encode this temporal context to guide HMM decoding. Furthermore, we introduce a progressive learning strategy with which neural networks could learn a robust recognition of HMM states in an iterative manner. The performance of our method is intensively evaluated on three challenging databases and achieves state-of-the-art results. Our method shows the abilities of both extracting the discriminate connotations and reducing large redundancy in the HMM transition process. It is verified that our framework can achieve online recognition of continuous gesture streams even when they are halfway performed |
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| Beschreibung: | Date Completed 29.10.2020 Date Revised 29.10.2020 published: Print-Electronic Citation Status PubMed-not-MEDLINE |
| ISSN: | 1941-0042 |
| DOI: | 10.1109/TIP.2020.3028962 |