Reference number : GC-2017-Q4-00176, amendment number : , agreement type : G, recipient legal name : Bilodeau, Guillaume-Alexandre (École Polytechnique de Montréal), 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 : This project aims at designing video analytic algorithms that will be implemented in a prototype smart camera._x000D_
They are: 1) Detection of an unauthorized person/intrusion and 2) Detection of dwelling. Although, several_x000D_
methods exist in the scientific literature for performing such tasks, most of them are either not very robust or_x000D_
not adapted for running on a embedded platform with more limited resources (processor and memory). Our_x000D_
partner company wishes to perform on an embedded system these tasks in real-time. Our investigations will_x000D_
therefore focus on researching methods and algorithms that can be implemented efficiently in a smart camera,_x000D_
yet that are robust, for detecting intrusion and dwelling in surveillance videos._x000D_
We will investigate how subsampling can be used to process fewer pixels per frame, but yet detect the objects_x000D_
of interest in background subtraction algorithms. We will also investigate the background update mechanisms_x000D_
to select or adapt only the most useful ones. Furthermore, we will design a motion history approach to_x000D_
accumulate the detected foreground detections. Decisions will be made based on the accumulation map using_x000D_
machine learning techniques., program purpose french : This project aims at designing video analytic algorithms that will be implemented in a prototype smart camera._x000D_
They are: 1) Detection of an unauthorized person/intrusion and 2) Detection of dwelling. Although, several_x000D_
methods exist in the scientific literature for performing such tasks, most of them are either not very robust or_x000D_
not adapted for running on a embedded platform with more limited resources (processor and memory). Our_x000D_
partner company wishes to perform on an embedded system these tasks in real-time. Our investigations will_x000D_
therefore focus on researching methods and algorithms that can be implemented efficiently in a smart camera,_x000D_
yet that are robust, for detecting intrusion and dwelling in surveillance videos._x000D_
We will investigate how subsampling can be used to process fewer pixels per frame, but yet detect the objects_x000D_
of interest in background subtraction algorithms. We will also investigate the background update mechanisms_x000D_
to select or adapt only the most useful ones. Furthermore, we will design a motion history approach to_x000D_
accumulate the detected foreground detections. Decisions will be made based on the accumulation map using_x000D_
machine learning techniques., agreement title english : Investigating resource efficient algorithms for monitoring in video surveillance, agreement title french : Investigating resource efficient algorithms for monitoring in video surveillance, agreement number : EGP, agreement value : 24700.0, agreement start date : 2018-02-07, 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