Analysis

Deep learning for detection and analysis in team sport images
In the context of indoor team sport events, detection and characterization of objects of interest for game strategy analysis and autonomous production and broadcasting from Convolutional Neural Networks.
Mar 25, 2019

Video Segmentation
Image/video segmentation aims at partitioning the visual frames into non-overlapping areas with different semantical contents. It has tremendous applications in data compression, tracking, augmented reality, activity or object recognition, video annotation and video retrieval. Our group focus on fast and efficient segmentation methods, in such a way to extract content with high-level of abstraction from videos.
Sep 1, 2017
Sport player foreground detector reinforcement through visual texture classification
In the context of sport events, this project deals with the improvement of a player detector based on a foreground detector. It considers some visual texture features in order to discriminate true from false detections.
Sep 1, 2017
Automatic Team Sport Coverage
Computer vision tools drive the automatic and democratic production of sport events video reports, by using scene analysis to select an appropriate viewpoint in a static and/or dynamic multi-camera infrastructure.
Sep 1, 2017

Ball detection and tracking: Foreground detector reinforcement through trajectory analysis
In the context of team sport events monitoring, the various phases of the game must be delimited and interpreted. In the case of a basketball game, the detection and the tracking of the ball are mandatory. However, one of the difficulties is that the ball is often occluded by players. This project deals with the detection of the ballistic trajectory of a ball thrown between two players or toward the basket. Ballistic trajectories are build on the 3D ball candidates previously detected at each time-stamp from a foreground detector.
Sep 1, 2016

Multiple object tracking with prior detections and graph formalisms
This project considers the tracking of multiple objects within video sequence(s). Fundamentally, it aims at formalising application scenarios in which reliability and the discriminability of the object features vary over time. In order to address problems involving large number of targets, and because automatic detection algorithms have gained maturity, our work assumes that a set of prior and plausible targets detections are available at each time instant.
Jul 1, 2013

Foreground object detection
Background modeling and foreground mask extraction are key components of low-level computer vision systems. They aim at extracting moving objects in natural scenes observed with static cameras, and thereby often constitute preliminary steps to object recognition, scene understanding and behavioral analysis.
Sep 1, 2012