Pierre-Etienne Martin

Currently Postdoctoral Researcher & Tech Development Coordinator at the Max Planck Institute for Evolutionary Anthropology in the Comparative Cultural Psychology department, I apply and develop computer vision tools with the aim of better understanding the human and non-human animal mind. We also aim to change our way of acquiring data by encouraging non-invasive methods. We hope to strengthen hypotheses and conclusions through big data analysis and model robustness.

Zoo Cam Set-Up

We aim to build a portable setup to locate, track and identify non-human primates in their enclosure or natural environment. Such a project can be divided into several independent tasks:

• individual detection & tracking

• track-line classification

• 3D localization from several images

In order to retrieve qualitative information, the Zoo Cam Set-Up must have synchronized high-resolution images and static cameras.

BioTIP - Bio-Signal Retrieval from Thermal Imaging Processing

Our focus is on building a set of computer vision tools for the psychology and animal behavior research communities to retrieve bio-signals in an ecological manner. We aim to increase the use of thermal imaging modality in the community and avoid using more invasive recording methods to answer research questions.

We created the ApeNose dataset, constituted of TI images of chimpanzees with their face (bbox) and nose (9 landmarks) annotated. Several state-of-the-art methods are then compared with our implemented Tifa (Face) and Tina (Nose) models. We aim to analyze the temporal evolution of certain regions and landmarks in order to retrieve physiological signals that help us better understand the human and non-human mind.

Quantex - Quantifying Lived Experience

Quantex focuses on the analysis of lived experience in childhood. For this purpose, families participate in our acquisition project by letting their child wear a vest equipped with a camera. In order to process and understand these egocentric videos, different computer vision challenges remain to be solved:

• object interaction detection

• person detection and identification

• action localization and classification

After have investigated the automatic detection of interaction with objects, we are focusing on the type of activities the child is performing alone or with others.

CASE - Computer-Animal Self-testing Environment

CASE aims to allow non-human animal individuals to test themselves independently in their enclosure at their own speed over several days. The state of the experiment should be saved when the individual leaves and loaded back when needed. Data acquisition can therefore be conducted without human intervention. The data may then be collected and processed remotely, as the experiment can be changed or updated. A dataset has been automatically generated using recordings from ZACI devices, automatic detection of individuals and low effort annotations. This dataset can be derived in several versions, leading to different performances for individual classification. This dataset is currently being enriched for further investigation.

• SAI Interview in 2023 - Video
• Climb the wall in 2023 - Article
• MediaEval Sport Task presentations from 2019 to 2023 - Youtube channel
• Ma thèse en 1024 caractères in 2020  - Article
• Ma thèse en 180 secondes in 2019 - MT180
• Jamming Assembly in 2019 - Video
• CNRS Village in 2019 - Picture
• The European Researchers’ Night in 2018 and 2019 - Website
• Discussion between Researchers and Secondary School Students (Declics) in 2018 - Website

DBLP - ArXiv - HAL

Pierre-Etienne Martin, Jenny Benois-Pineau, Renaud Péteri, Julien Morlier. Fine grained sport action recognition with Twin spatio-temporal convolutional neural networks. Multimedia Tools and Applications, Springer Verlag, 2020.
DOI  hal-02551019

Pierre-Etienne Martin, Jenny Benois-Pineau, Renaud Péteri, Julien Morlier. 3D attention mechanism for fine-grained classification of table tennis strokes using a Twin Spatio-Temporal Convolutional Neural Networks. 25th International Conference on Pattern Recognition (ICPR2020), Jan 2021, Milano, Italy.
hal-02977646

Pierre-Etienne Martin, Jenny Benois-Pineau, Boris Mansencal, Renaud Péteri, Laurent Mascarilla, et al.. Sports Video Classification: Classification of Strokes in Table Tennis for MediaEval 2020. MediaEval 2020 Workshop, Jan 2021, Online, Unknown Region.
hal-03104270

Pierre-Etienne Martin, Jenny Benois-Pineau, Boris Mansencal, Renaud Péteri, Julien Morlier. Classification of Strokes in Table Tennis with a Three Stream Spatio-Temporal CNN for MediaEval 2020. MediaEval 2020 Workshop, Dec 2020, Online, Unknown Region.
hal-03104275

Kazi Ahmed Asif Fuad, Pierre-Etienne Martin, Romain Giot, Romain Bourqui, Jenny Benois-Pineau, et al.. Features Understanding in 3D CNNs for Actions Recognition in Video. Tenth International Conference on Image Processing Theory, Tools and Applications, IPTA 2020, Oct 2020, Paris, France.
hal-02963298

Pierre-Etienne Martin, Jenny Benois-Pineau, Renaud Peteri, Julien Morlier. Optimal Choice of Motion Estimation Methods for Fine-Grained Action Classification with 3D Convolutional Networks. 2019 IEEE International Conference on Image Processing (ICIP), Sep 2019, Taipei, Taiwan. pp.554-558.
10.1109/ICIP.2019.8803780  hal-02326240

Pierre-Etienne Martin, Jenny Benois-Pineau, Renaud Péteri. Fine-Grained Action Detection and Classification in Table Tennis with Siamese Spatio-Temporal Convolutional Neural Network. 2019 IEEE International Conference on Image Processing (ICIP), Sep 2019, Taipei, Taiwan. pp.3027-3028.
10.1109/ICIP.2019.8803382  hal-02326229

Pierre-Etienne Martin, Jenny Benois-Pineau, Boris Mansencal, Renaud Péteri, Jordan Calandre, et al.. Sports Video Annotation: Detection of Strokes in Table Tennis task for MediaEval 2019. MediaEval 2019 Workshop, Oct 2019, Sophia Antipolis, France.
hal-02937666

Pierre-Etienne Martin, Jenny Benois-Pineau, Boris Mansencal, Renaud Péteri, Julien Morlier. Siamese Spatio-temporal convolutional neural network for stroke classification in Table Tennis games. MediaEval 2019 Workshop, Oct 2019, Sophia Antipolis, France.
hal-02937668

Pierre-Etienne Martin, Jenny Benois-Pineau, Renaud Péteri, Julien Morlier. Sport Action Recognition with Siamese Spatio-Temporal CNNs: Application to Table Tennis. 2018 International Conference on Content-Based Multimedia Indexing (CBMI), Sep 2018, La Rochelle, France. pp.1-6,
10.1109/CBMI.2018.8516488  hal-02360011

PhD Thesis

Pierre-Etienne Martin. Fine-Grained Action Detection and Classification from Videos with Spatio-Temporal Convolutional Neural Networks. Application to Table Tennis.. Neural and Evolutionary Computing [cs.NE]. Université de Bordeaux; Université de la Rochelle, 2020. English.
tel-03099907

Master Thesis

Pierre-Etienne Martin. Face recognition using depth information. Master thesis, Institute of Thermophysics of Novosibirsk, Russia, 2017.