Reference number : GC-2017-Q4-00340, amendment number : , agreement type : G, recipient legal name : Chiang, Fei Yen (McMaster University), recipient country : CA, recipient province : nan, recipient city : Ontario, recipient postal code : nan, program name english : Collaborative Research and Development Grants - Project, program name french : Subventions de recherche et développement coopérative - projet, program purpose english : Locating and tracking people's activities in indoor environments are crucial to location-based services (LBS) such as indoor wayfinding, proximity-based advertisement, workforce management, geo-fencing, fraud detection, etc. Existing indoor position systems (IPS) fall short in cost, accuracy, usability, understanding of interactions between users and environment, and working in device-free settings. The project builds upon our extensive experience in IPS, proximity technologies, and data fusion to develop a multimodal IPS solution at sub-meter accuracy that can be used in a variety of markets (e.g, retail, health, warehouse, etc.). Our envisioned platform consists of the following components: (1) a distributed camera network that monitors the areas of interest, along with mobile sensors on personal devices;  (2) real-time vision data processing and localization to identify and track objects as well as people's activities; and (3) a data preparation and analysis engine that cleans and curates the data for analytics. In this proposal, we will address both algorithmic and computational challenges posed by processing in real-time, multi-modal streaming data to develop an accurate and reliable indoor localization platform._x000D_
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_x000D_, program purpose french : Locating and tracking people's activities in indoor environments are crucial to location-based services (LBS) such as indoor wayfinding, proximity-based advertisement, workforce management, geo-fencing, fraud detection, etc. Existing indoor position systems (IPS) fall short in cost, accuracy, usability, understanding of interactions between users and environment, and working in device-free settings. The project builds upon our extensive experience in IPS, proximity technologies, and data fusion to develop a multimodal IPS solution at sub-meter accuracy that can be used in a variety of markets (e.g, retail, health, warehouse, etc.). Our envisioned platform consists of the following components: (1) a distributed camera network that monitors the areas of interest, along with mobile sensors on personal devices;  (2) real-time vision data processing and localization to identify and track objects as well as people's activities; and (3) a data preparation and analysis engine that cleans and curates the data for analytics. In this proposal, we will address both algorithmic and computational challenges posed by processing in real-time, multi-modal streaming data to develop an accurate and reliable indoor localization platform._x000D_
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_x000D_, agreement title english : A data-driven platform for multimodal positioning and tracking in indoor environments, agreement title french : A data-driven platform for multimodal positioning and tracking in indoor environments, agreement number : CRDPJ, agreement value : 460000.0, 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 2020-2021)., additional information french : Subvention ou bourse octroyée s'appliquant à plus d'un exercice financier (2017-2018 à 2020-2021)., 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