Publications by Andrii Maksai | EPFL Graph Search

Training Algorithms for Multiple Object Tracking

Multiple object tracking is a crucial Computer Vision Task. It aims at locating objects of interest in the image sequences, maintaining their identities, and identifying their trajectories over time. A large portion of current research focuses on tracking ...

EPFL2019

Eliminating Exposure Bias and Metric Mismatch in Multiple Object Tracking

Pascal Fua, Andrii Maksai

Identity Switching remains one of the main difficulties Multiple Object Tracking (MOT) algorithms have to deal with. Many state-of-the-art approaches now use sequence models to solve this problem but their training can be affected by biases that decrease t ...

IEEE2019

WILDTRACK: A Multi-camera HD Dataset for Dense Unscripted Pedestrian Detection

Pascal Fua, Tatjana Chavdarova, François Fleuret, Andrii Maksai, Cijo Jose, Stéphane Bouquet

People detection methods are highly sensitive to occlusions between pedestrians, which are extremely frequent in many situations where cameras have to be mounted at a limited height. The reduction of camera prices allows for the generalization of static mu ...

IEEE2018

What Players do with the Ball: A Physically Constrained Interaction Modeling

Pascal Fua, Xinchao Wang, Andrii Maksai

Tracking the ball is critical for video-based analysis of team sports. However, it is difficult, especially in low resolution images, due to the small size of the ball, its speed that creates motion blur, and its often being occluded by players. In this pa ...

2016

Predicting Online Performance of News Recommender Systems Through Richer Evaluation Metrics

Boi Faltings, Florent Frédéric Garcin, Andrii Maksai

We investigate how metrics that can be measured offline can be used to predict the online performance of recommender systems, thus avoiding costly A-B testing. In addition to accuracy metrics, we combine diversity, coverage, and serendipity metrics to crea ...

2015