Abstract
Online action recognition is nowadays a major challenge on computer vision due to uncontrolled scenarios, variability on dynamic action representations, unrestricted capture protocols among many other variations. This work introduces a very compact binary occurrence motion descriptor that allows to recognize actions on partial video-sequences. The proposed approach starts by computing a set of motion trajectories that represent the developed activity. On that regard, a local counting process is performed over bounded regions, and centered at each trajectory, to search for a minimal number of neighboring trajectories. This process is then codified in a vector of binary values (ToBPs) that will create a regional description, at any time of the video sequence, to represent actions. This regional description is obtained by determining the most recurrent binary descriptors in a particular video interval. The final regional descriptor is mapped to a machine learning algorithm to obtain a recognition. The proposed strategy was evaluated on three public datasets, achieving an average accuracy of 70% in tasks of action recognition by using a local descriptor of only 51 values and a regional descriptor of 400 values. This compact description constitute an ideal condition for real-time video applications. The proposed approach achieves a partial recognition above 70% on average accuracy using only the 40% of videos.
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Acknowledgements
This work was partially funded by the Universidad Industrial de Santander. The authors acknowledge the Decanato de la Facultad de Ingenierías Fisicomecánicas and the Vicerrectoría de Investigación y Extensión (VIE) of the Universidad Industrial de Santander for supporting this research registered by the project: Reconocimiento continuo de expresiones cortas del lenguaje de señas, with SIVIE code 2430.
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Garzón, G., Martínez, F. (2020). Online Action Recognition from Trajectory Occurrence Binary Patterns (ToBPs). In: Botto-Tobar, M., León-Acurio, J., Díaz Cadena, A., Montiel Díaz, P. (eds) Advances in Emerging Trends and Technologies. ICAETT 2019. Advances in Intelligent Systems and Computing, vol 1066. Springer, Cham. https://doi.org/10.1007/978-3-030-32022-5_38
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DOI: https://doi.org/10.1007/978-3-030-32022-5_38
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