13 citations · 20 across the 9 of their papers we have counts for
10 papers
Dataset Generation and Bonobo Classification from Weakly Labelled Videos
Pierre-Etienne Martin
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…
Fine-Grained Action Detection with RGB and Pose Information using Two Stream Convolutional Networks
Leonard Hacker, Finn Bartels, Pierre-Etienne Martin
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…
Baseline Method for the Sport Task of MediaEval 2022 with 3D CNNs using Attention Mechanisms
Pierre-Etienne Martin
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…
Sport Task: Fine Grained Action Detection and Classification of Table Tennis Strokes from Videos for MediaEval 2022
Pierre-Etienne Martin, Jordan Calandre, Boris Mansencal +4
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 a…
3D Convolutional Networks for Action Recognition: Application to Sport Gesture Recognition
Pierre-Etienne Martin, J Benois-Pineau, R Péteri +2
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 taxono…
Spatio-Temporal CNN baseline method for the Sports Video Task of MediaEval 2021 benchmark
Pierre-Etienne Martin
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 s…
Two Stream Network for Stroke Detection in Table Tennis
Anam Zahra, Pierre-Etienne Martin
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…
Sports Video: Fine-Grained Action Detection and Classification of Table Tennis Strokes from Videos for MediaEval 2021
Pierre-Etienne Martin, Jordan Calandre, Boris Mansencal +4
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 o…
Three-Stream 3D/1D CNN for Fine-Grained Action Classification and Segmentation in Table Tennis
Pierre-Etienne Martin, Jenny Benois-Pineau, Renaud Péteri +1
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 classif…
3D attention mechanism for fine-grained classification of table tennis strokes using a Twin Spatio-Temporal Convolutional Neural Networks
Pierre-Etienne Martin, Jenny Benois-Pineau, Renaud Péteri +1
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 a…