Developing intelligent video surveillance systems that are able to automatically detect abnormal events requires solving challenging computer vision problems, such as object detection, tracking, and image recognition. During recent years, such problems have been addressed by increasingly more robust algorithms, producing impressive performances on standard benchmarks. However, this progress is not as impressive in realworld applications, where the vast majority of video surveillance systems still require human attention and manual intervention. For this reason, we argue that a significant gap exists between the work on fundamental problems of computer vision, and the development of intelligent video surveillance applications. The present research aims at narrowing this gap, by enhancing the intelligence of video surveillance systems.
CRSNG (Conseil de recherches en sciences naturelles et en génie du Canada)
Subvention à la découverte individuelle et subvention tremplin vers la découverte
Secteur de recherche
Systèmes intelligents, sciences et technologies de l'information
2020 - 2025
132 500,00 $