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of 51
pro vyhledávání: '"Martin, Pierre‐Etienne"'
Autor:
Martin, Pierre-Etienne
This paper presents a bonobo detection and classification pipeline built from the commonly used machine learning methods. Such application is motivated by the need to test bonobos in their enclosure using touch screen devices without human assistance
Externí odkaz:
http://arxiv.org/abs/2309.03671
As participants of the MediaEval 2022 Sport Task, we propose a two-stream network approach for the classification and detection of table tennis strokes. Each stream is a succession of 3D Convolutional Neural Network (CNN) blocks using attention mecha
Externí odkaz:
http://arxiv.org/abs/2302.02755
Autor:
Martin, Pierre-Etienne
This paper presents the baseline method proposed for the Sports Video task part of the MediaEval 2022 benchmark. This task proposes two subtasks: stroke classification from trimmed videos, and stroke detection from untrimmed videos. This baseline add
Externí odkaz:
http://arxiv.org/abs/2302.02752
Autor:
Martin, Pierre-Etienne, Calandre, Jordan, Mansencal, Boris, Benois-Pineau, Jenny, Péteri, Renaud, Mascarilla, Laurent, Morlier, Julien
Sports video analysis is a widespread research topic. Its applications are very diverse, like events detection during a match, video summary, or fine-grained movement analysis of athletes. As part of the MediaEval 2022 benchmarking initiative, this t
Externí odkaz:
http://arxiv.org/abs/2301.13576
3D convolutional networks is a good means to perform tasks such as video segmentation into coherent spatio-temporal chunks and classification of them with regard to a target taxonomy. In the chapter we are interested in the classification of continuo
Externí odkaz:
http://arxiv.org/abs/2204.08460
Autor:
Martin, Pierre-Etienne, Calandre, Jordan, Mansencal, Boris, Benois-Pineau, Jenny, Péteri, Renaud, Mascarilla, Laurent, Morlier, Julien
Sports video analysis is a prevalent research topic due to the variety of application areas, ranging from multimedia intelligent devices with user-tailored digests up to analysis of athletes' performance. The Sports Video task is part of the MediaEva
Externí odkaz:
http://arxiv.org/abs/2112.11384
Autor:
Martin, Pierre-Etienne
Publikováno v:
MediaEval 2021, Dec 2021, Online, Germany
This paper presents the baseline method proposed for the Sports Video task part of the MediaEval 2021 benchmark. This task proposes a stroke detection and a stroke classification subtasks. This baseline addresses both subtasks. The spatio-temporal CN
Externí odkaz:
http://arxiv.org/abs/2112.12074
Autor:
Zahra, Anam, Martin, Pierre-Etienne
This paper presents a table tennis stroke detection method from videos. The method relies on a two-stream Convolutional Neural Network processing in parallel the RGB Stream and its computed optical flow. The method has been developed as part of the M
Externí odkaz:
http://arxiv.org/abs/2112.12073
This paper proposes a fusion method of modalities extracted from video through a three-stream network with spatio-temporal and temporal convolutions for fine-grained action classification in sport. It is applied to TTStroke-21 dataset which consists
Externí odkaz:
http://arxiv.org/abs/2109.14306
Publikováno v:
25th International Conference on Pattern Recognition (ICPR2020), Jan 2021, Milano, Italy
The paper addresses the problem of recognition of actions in video with low inter-class variability such as Table Tennis strokes. Two stream, "twin" convolutional neural networks are used with 3D convolutions both on RGB data and optical flow. Action
Externí odkaz:
http://arxiv.org/abs/2012.05342