Reference number : GC-2017-Q2-00504, amendment number : , agreement type : G, recipient legal name : Clark, James (McGill University)|Clark, James (Université McGill), 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 : The project involved improving the performance and enhancing the capabilities of the BriskVANTAGE traffic_x000D_
analysis software from the BriskSynergies company. Modern state-of-the-art machine learning technology_x000D_
(deep convolutional neural networks) will be used to improve the detection and recognition of objects such as_x000D_
motorcycles, bicycles, trucks and buses, in addition to automobiles and pedestrians. The deep net algorithms_x000D_
will be modified to include robust object tracking and data association (retaining the identity of which scene_x000D_
object is which when tracking) in situations where vehicles and pedestrians may become occluded by other_x000D_
objects, or when tracking is momentarily lost. Benefits to Canada for the improved BriskVANTAGE software_x000D_
includes higher quality and higher reliability data analytics for urban planners engaged in traffic monitoring and_x000D_
analysis, leading to safer roads and improved traffic flow. Real-time systems can provide immediate signaling_x000D_
of significant traffic events as well as long-term monitoring of traffic and safety conditions and emergent trends_x000D_
due to changes in demographics and road construction., program purpose french : The project involved improving the performance and enhancing the capabilities of the BriskVANTAGE traffic_x000D_
analysis software from the BriskSynergies company. Modern state-of-the-art machine learning technology_x000D_
(deep convolutional neural networks) will be used to improve the detection and recognition of objects such as_x000D_
motorcycles, bicycles, trucks and buses, in addition to automobiles and pedestrians. The deep net algorithms_x000D_
will be modified to include robust object tracking and data association (retaining the identity of which scene_x000D_
object is which when tracking) in situations where vehicles and pedestrians may become occluded by other_x000D_
objects, or when tracking is momentarily lost. Benefits to Canada for the improved BriskVANTAGE software_x000D_
includes higher quality and higher reliability data analytics for urban planners engaged in traffic monitoring and_x000D_
analysis, leading to safer roads and improved traffic flow. Real-time systems can provide immediate signaling_x000D_
of significant traffic events as well as long-term monitoring of traffic and safety conditions and emergent trends_x000D_
due to changes in demographics and road construction., agreement title english : Deep Learning for analyzing traffic camera video, agreement title french : Deep Learning for analyzing traffic camera video, agreement number : EGP, agreement value : 25000.0, agreement start date : 2017-08-23, 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