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- Papadopoulos, K., et al.
(författare)
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A revisit of action detection using improved trajectories
- 2018
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Ingår i: ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 2067-2071
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Konferensbidrag (refereegranskat)abstract
- In this paper, we revisit trajectory-based action detection in a potent and non-uniform way. Improved trajectories have been proven to be an effective model for motion description in action recognition. In temporal action localization, however, this approach is not efficiently exploited. Trajectory features extracted from uniform video segments result in significant performance degradation due to two reasons: (a) during uniform segmentation, a significant amount of noise is often added to the main action and (b) partial actions can have negative impact in classifier's performance. Since uniform video segmentation seems to be insufficient for this task, we propose a two-step supervised non-uniform segmentation, performed in an online manner. Action proposals are generated using either 2D or 3D data, therefore action classification can be directly performed on them using the standard improved trajectories approach. We experimentally compare our method with other approaches and we show improved performance on a challenging online action detection dataset.
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- Papadopoulos, K., et al.
(författare)
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Enhanced trajectory-based action recognition using human pose
- 2018
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Ingår i: Proceedings - International Conference on Image Processing, ICIP. - : Institute of Electrical and Electronics Engineers (IEEE). ; , s. 1807-1811
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Konferensbidrag (refereegranskat)abstract
- Action recognition using dense trajectories is a popular concept. However, many spatio-temporal characteristics of the trajectories are lost in the final video representation when using a single Bag-of-Words model. Also, there is a significant amount of extracted trajectory features that are actually irrelevant to the activity being analyzed, which can considerably degrade the recognition performance. In this paper, we propose a human-tailored trajectory extraction scheme, in which trajectories are clustered using information from the human pose. Two configurations are considered; first, when exact skeleton joint positions are provided, and second, when only an estimate thereof is available. In both cases, the proposed method is further strengthened by using the concept of local Bag-of-Words, where a specific codebook is generated for each skeleton joint group. This has the advantage of adding spatial human pose awareness in the video representation, effectively increasing its discriminative power. We experimentally compare the proposed method with the standard dense trajectories approach on two challenging datasets.
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- Papadopoulos, K., et al.
(författare)
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Localized trajectories for 2D and 3D action recognition
- 2019
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Ingår i: Sensors. - : MDPI AG. - 1424-8220. ; 19:16
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Tidskriftsartikel (refereegranskat)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.
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- Papadopoulos, K., et al.
(författare)
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Two-Stage RGB-Based Action Detection Using Augmented 3D Poses
- 2019
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Ingår i: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). - Cham : Springer International Publishing. ; , s. 26-35
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Konferensbidrag (refereegranskat)
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