| Full text | |
| Author(s): |
Ximenes, Elias
;
Pedrini, Helio
;
IEEE
Total Authors: 3
|
| Document type: | Journal article |
| Source: | 2019 IEEE INTERNATIONAL CONFERENCE ON SYSTEMS, MAN AND CYBERNETICS (SMC); v. N/A, p. 6-pg., 2019-01-01. |
| Abstract | |
A hand tracking method is presented in this work, which achieves the best results found in the literature for the public RWTH-BOSTON-50 dataset, with a tracking error rate of 8.5%. Its main contribution is the extraction of intrinsic features from RGB sign language movies. In order to avoid some common limitations of model and appearance-based tracking methods, a movement pattern analysis was used as a feature basis. Such feature can be succinctly described as a space-time graph of interest point movement similarity, which is arranged as trees of dense trajectory connections to track hands in RGB sign language movies. In addition to basic geometry operations, simple graph methods are employed in the process, making it effective for parallel processing of large movie datasets. (AU) | |
| FAPESP's process: | 17/12646-3 - Déjà vu: feature-space-time coherence from heterogeneous data for media integrity analytics and interpretation of events |
| Grantee: | Anderson de Rezende Rocha |
| Support Opportunities: | Research Projects - Thematic Grants |