Dempster-Shafer based multi-view occupancy maps

Presented is a method for calculating occupancy maps with a set of calibrated and synchronised cameras. In particular, Dempster-Shafer based fusion of the ground occupancies computed from each view is proposed. The method yields very accurate occupancy detection results and in terms of concentration of the occupancy evidence around ground truth person positions it outperforms the state-of-the- art probabilistic occupancy map method and fusion by summing

Efficient tracking of team sport players with few game-specific annotations

One of the requirements for team sports analysis is to track and recognize players. Many tracking and reidentification methods have been proposed in the context of video surveillance. They show very convincing results when tested on public datasets such as the MOT challenge. However, the performance of these methods are not as satisfactory when applied to player tracking. Indeed, in addition to moving very quickly and often being occluded, the players wear the same jersey, which makes the task of reidentification very complex. Some recent tracking methods have been developed more specifically for the team sport context. Due to the lack of public data, these methods use private datasets that make impossible a comparison with them. In this paper, we propose a new generic method to track team sport players during a full game thanks to few human annotations collected via a semi-interactive system. Non-ambiguous tracklets and their appearance features are automatically generated with a detection and a reidentification network both pre-trained on public datasets. Then an incremental learning mechanism trains a Transformer to classify identities using few game-specific human annotations. Finally, tracklets are linked by an association algorithm. We demonstrate the efficiency of our approach on a challenging rugby sevens dataset. To overcome the lack of public sports tracking dataset, we publicly release this dataset at https://kalisteo.cea.fr/index.php/free-resources/. We also show that our method is able to track rugby sevens players during a full match, if they are observable at a minimal resolution, with the annotation of only 6 few seconds length tracklets per player.Comment: Accepted to 2022 8th International Workshop on Computer Vision in Sports (CVsports 2022

A video-based framework for automatic 3d localization of multiple basketball players : a combinatorial optimization approach

Sports complexity must be investigated at competitions; therefore, non-invasive methods are essential. In this context, computer vision, image processing, and machine learning techniques can be useful in designing a non-invasive system for data acquisition that identifies players’ positions in official basketball matches. Here, we propose and evaluate a novel video-based framework to perform automatic 3D localization of multiple basketball players. The introduced framework comprises two parts. The first stage is player detection, which aims to identify players’ heads at the camera image level. This stage is based on background segmentation and on classification performed by an artificial neural network. The second stage is related to 3D reconstruction of the player positions from the images provided by the different cameras used in the acquisition. This task is tackled by formulating a constrained combinatorial optimization problem that minimizes the re-projection error while maximizing the number of detections in the formulated 3D localization problem8286CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQCOORDENAÇÃO DE APERFEIÇOAMENTO DE PESSOAL DE NÍVEL SUPERIOR - CAPESFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPNão temNão temNão temWe would like to thank the CAPES, FAEPEX, FAPESP, and CNPq for funding their research. This paper has content from master degree’s dissertation previously published (Monezi, 2016) and available onlin

Associative Embedding for Game-Agnostic Team Discrimination

Assigning team labels to players in a sport game is not a trivial task when no prior is known about the visual appearance of each team. Our work builds on a Convolutional Neural Network (CNN) to learn a descriptor, namely a pixel-wise embedding vector, that is similar for pixels depicting players from the same team, and dissimilar when pixels correspond to distinct teams. The advantage of this idea is that no per-game learning is needed, allowing efficient team discrimination as soon as the game starts. In principle, the approach follows the associative embedding framework introduced in arXiv:1611.05424 to differentiate instances of objects. Our work is however different in that it derives the embeddings from a lightweight segmentation network and, more fundamentally, because it considers the assignment of the same embedding to unconnected pixels, as required by pixels of distinct players from the same team. Excellent results, both in terms of team labelling accuracy and generalization to new games/arenas, have been achieved on panoramic views of a large variety of basketball games involving players interactions and occlusions. This makes our method a good candidate to integrate team separation in many CNN-based sport analytics pipelines.Comment: Published in CVPR 2019 workshop Computer Vision in Sports, under the name "Associative Embedding for Team Discrimination" (http://openaccess.thecvf.com/content_CVPRW_2019/html/CVSports/Istasse_Associative_Embedding_for_Team_Discrimination_CVPRW_2019_paper.html

Comparison of methods for the number recognition of sport players

Projecte final de carrera fet en col.laboració amb Université Catholique de Louvain. Ecole Polytechnique de Louvain.In a system of detection and recognition of players on a sport- el, the process of identifying numbers from a binary image is the last step. The identi cation is possible using the numbers of players jerseys that are obtained from images captured by a distributed set of cameras. The main problem to solve is that these pictures have di erent sizes and su er rotations and non-rigid deformations. Prior to this project were independently studied three methods to recognize numbers from the players jerseys. The good results obtained encouraged to use any of these methods in the system of recognition of athletes. The provided methods to obtain the image features are based on matching pursuit, local binary pattern and optical character recognition algorithms. The main objective of this project is justify which is the most appropriate method. But the fact that previous studies were independents involve that can not be made a direct comparison of their results. Therefore is necessary a fair comparison with common criteria. Get this fair comparison is result of working in an homogenous environment. This implies that all the methods have been tested with a set of common images and a common classi er, based on support vector machine. It has been trained with a set of pictures of a basketball match. The criteria taken into account have been the accuracy in the identi cation, obtained with the 10-fold cross-validacion, and the time needed by each method to identify a number. Once done the study, it has been concluded that the method that works with the optical character recognition is the most accurate and fastest. Although this method is the most susceptible to the rotation of images, so its need a normalization, clearly is the most e cient as for the identi cation of digits as to detect a false identi cation. The fact that the di erences between the three methods results are so wide, leads to be sure of the choice made. So clearly has been chosen the most e cient method to be used in the project, still in development, that detects, recognizes and tracks players on a sport- eld

Health and the primary prevention of violence against women

This position paper focuses on the primary prevention of violence perpetrated by men against women. It develops a position on primary prevention (as distinct from secondary and tertiary interventions). It also identifies examples of good practice across settings, and factors for success for primary prevention programs. The paper has been developed as a resource for public education, debate and community activities related to the primary prevention of violence against women.Intimate partner violence is prevalent, serious and preventable; it is also a crime. Among the poor health outcomes for women who experience intimate partner violence are premature death and injury, poor mental health, habits which are harmful to health such as smoking, misuse of alcohol and non-prescription drugs, use of tranquilisers, sleeping pills and anti-depressants.  The cost of violence against women to individuals, communities and the whole of society is staggering and unacceptable. Every week in Australia at least one woman is killed by her current or former partner, and since the age of 15, one in three women has experienced physical violence and one in five has experienced sexual violence. The annual financial cost to the community of violence against women was calculated by Access Economics in 2002/3 to be $8.1 billion (Victorian Health Promotion Foundation, 2004), a figure which is likely to increase unless the incidence of violence against women can be reduced and ultimately eliminated.&nbsp