Technical Gestures Recognition by Set-Valued Hidden Markov Models with Prior Knowledge
Hidden Markov models are popular tools for gesture recognition. Once the generative processes of gestures have been identified, an observation sequence is usually classified as the gesture having the highest likelihood, thus ignoring possible prior information. In this paper, we consider two potential improvements of such methods: the inclusion of prior information, and the possibility of considering convex sets of probabilities (in the likelihoods and the prior) to infer imprecise, but more reliable, predictions when information is insufficient. We apply the proposed approach to technical gestures, typically characterized by severe class imbalance. By modelling such imbalances as a prior information, we achieve more accurate results, while the imprecise quantification is shown to produce more reliable estimates.
KeywordsWelding Covariance Mold Production Line
This work is founded by the European Union and the French region Picardie. Europe acts in Picardie with the European Regional Development Fund (ERDF). This work is supported by the ANR UML-net project, grant ANR-14-CE24-0026 of the French Agence Nationale de la Recherche (ANR).
- 1.Antonucci A, de Rosa R, Giusti A, Cuzzolin F (2015) Robust classification of multivariate time series by imprecise hidden Markov models. Int J Approx Reason 56(B):249–263Google Scholar
- 2.Bevilacqua F, Zamborlin B, Sypniewski A, Schnell N, Guédy F, Rasamimanana N (2010) Continuous Realtime Gesture Following and Recognition. In: Gesture in embodied communication and human-computer interaction: 8th international gesture workshop, GW 2009, Revised Selected Papers. Springer, pp 73–84Google Scholar
- 3.Bouchard G, Triggs B (2004) The tradeoff between generative and discriminative classifiers. In: International symposium on computational statistics, pp 721–728Google Scholar
- 6.Liu K, Chen C, Jafari R, Kehtarnavaz N (2014) Multi-HMM classification for hand gesture recognition using two differing modality sensors. In: Circuits and systems conference (DCAS). IEEE, pp 1–4Google Scholar
- 8.Neverova N, Wolf C, Taylor GW, Nebout F (2014) Multi-scale deep learning for gesture detection and localization. In: ECCV workshop on looking at peopleGoogle Scholar