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Open AccessArticle

Localized Trajectories for 2D and 3D Action Recognition

1
Interdisciplinary Center for Security, Reliability and Trust, University of Luxembourg, L-1855 Luxembourg, Luxembourg
2
Perceive3D, 3030-199 Coimbra, Portugal
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Author to whom correspondence should be addressed.
This paper is an expanded version of “Enhanced trajectory-based action recognition using human pose” published in 2017 IEEE International Conference on Image Processing (ICIP), Beijing, China, 17–20 September 2017.
Sensors 2019, 19(16), 3503; https://doi.org/10.3390/s19163503
Received: 23 May 2019 / Revised: 31 July 2019 / Accepted: 7 August 2019 / Published: 10 August 2019
(This article belongs to the Section Physical Sensors)
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Abstract

The Dense Trajectories concept is one of the most successful approaches in action recognition, suitable for scenarios involving a significant amount of motion. However, due to noise and background motion, many generated trajectories are irrelevant to the actual human activity and can potentially lead to performance degradation. In this paper, we propose Localized Trajectories as an improved version of Dense Trajectories where motion trajectories are clustered around human body joints provided by RGB-D cameras and then encoded by local Bag-of-Words. As a result, the Localized Trajectories concept provides an advanced discriminative representation of actions. Moreover, we generalize Localized Trajectories to 3D by using the depth modality. One of the main advantages of 3D Localized Trajectories is that they describe radial displacements that are perpendicular to the image plane. Extensive experiments and analysis were carried out on five different datasets. View Full-Text
Keywords: action recognition; Dense Trajectories; Local Bag-of-Words; spatiotemporal features action recognition; Dense Trajectories; Local Bag-of-Words; spatiotemporal features
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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MDPI and ACS Style

Papadopoulos, K.; Demisse, G.; Ghorbel, E.; Antunes, M.; Aouada, D.; Ottersten, B. Localized Trajectories for 2D and 3D Action Recognition. Sensors 2019, 19, 3503.

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