Reference number : GC-2017-Q4-00692, amendment number : , agreement type : G, recipient legal name : Granger, Eric (École de technologie supérieure), recipient country : CA, recipient province : nan, recipient city : Quebec|Québec, recipient postal code : nan, program name english : Engage Grants for universities, program name french : Subventions d'engagement partenarial pour les universités, program purpose english : SPORTLOGiQ Inc. develops advanced sports analytics software to track the location and actions of players_x000D_
appearing in sports game videos. This project focuses on the task of person re-identification in sports videos,_x000D_
where the same players should be recognized across multiple distributed cameras viewpoint, or across time_x000D_
within a single camera. In this context, the performance of person re-identification algorithms can decline due_x000D_
to variations in player capture conditions, background clutter, and occlusions. Computational complexity is_x000D_
another important consideration for real-time applications._x000D_
This project aims to provide SPORTLOGiQ with the expertise needed to design accurate yet efficient systems_x000D_
for real-time person re-identification. Given the state-of-the-art accuracy achieved with deep learning_x000D_
architectures on many challenging visual recognition problems, SPORTLOGiQ seeks to develop and evaluate_x000D_
deep convolutional neural networks (CNNs) for accurate person re-identification in sports videos. However,_x000D_
since these CNNs represent complex solutions for real-time applications, this project seeks to develop_x000D_
specialized techniques for compression of CNNs, to reduce their time and memory complexity. In this project,_x000D_
the performance of several techniques proposed in the literature to increase the computational efficiency of_x000D_
CNNs will be evaluated and compared for real-time person re-identification on sports game videos. These_x000D_
include advanced techniques for search reduction, features selection, and parameter pruning. In particular, this_x000D_
project will focus mostly on promising new filter-level pruning techniques that can simultaneously accelerate_x000D_
and compress a CNN based on statistical information extracted from its layers. The techniques evaluated in this_x000D_
project are of great interest to SPORTLOGiQ and to the computer vision and machine learning communities in_x000D_
general. The aim of this is research project is to solve one of the fundamental computer vision problems in the_x000D_
context of sports video analytics., program purpose french : SPORTLOGiQ Inc. develops advanced sports analytics software to track the location and actions of players_x000D_
appearing in sports game videos. This project focuses on the task of person re-identification in sports videos,_x000D_
where the same players should be recognized across multiple distributed cameras viewpoint, or across time_x000D_
within a single camera. In this context, the performance of person re-identification algorithms can decline due_x000D_
to variations in player capture conditions, background clutter, and occlusions. Computational complexity is_x000D_
another important consideration for real-time applications._x000D_
This project aims to provide SPORTLOGiQ with the expertise needed to design accurate yet efficient systems_x000D_
for real-time person re-identification. Given the state-of-the-art accuracy achieved with deep learning_x000D_
architectures on many challenging visual recognition problems, SPORTLOGiQ seeks to develop and evaluate_x000D_
deep convolutional neural networks (CNNs) for accurate person re-identification in sports videos. However,_x000D_
since these CNNs represent complex solutions for real-time applications, this project seeks to develop_x000D_
specialized techniques for compression of CNNs, to reduce their time and memory complexity. In this project,_x000D_
the performance of several techniques proposed in the literature to increase the computational efficiency of_x000D_
CNNs will be evaluated and compared for real-time person re-identification on sports game videos. These_x000D_
include advanced techniques for search reduction, features selection, and parameter pruning. In particular, this_x000D_
project will focus mostly on promising new filter-level pruning techniques that can simultaneously accelerate_x000D_
and compress a CNN based on statistical information extracted from its layers. The techniques evaluated in this_x000D_
project are of great interest to SPORTLOGiQ and to the computer vision and machine learning communities in_x000D_
general. The aim of this is research project is to solve one of the fundamental computer vision problems in the_x000D_
context of sports video analytics., agreement title english : Compression of Convolutional Neural Networks for Efficient Real-Time Person Re-Identification Applications, agreement title french : Compression of Convolutional Neural Networks for Efficient Real-Time Person Re-Identification Applications, agreement number : EGP, agreement value : 25000.0, agreement start date : 2018-03-07, agreement end date : nan, description english : nan, description french : nan, expected results english : nan, expected results french : nan, additional information english : Grant or Award spanning more than one fiscal year (2017-2018 to 2018-2019)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2018-2019)., owner organization : nserc-crsng, owner organization title : Natural Sciences and Engineering Research Council of Canada | Conseil de recherches en sciences naturelles et en génie du Canada