The aim of the project is to develop a multi-camera monitoring and profiling system for crowded environments, relying on demographics information and face re-identification approaches.View Project
MAPPER. Project co-financed under Tuscany POR FESR 2014-2020. People counting, tracking and identification system for security and access control.
funded by: Higoal srls
Higoal project aims at realizing an automated video shooting system which is able to pan and zoom on the playing field according to the game events and to select salient segments creating a short clip which summarizes the major events of the match. The system is built up of two fixed surveillance cameras, one for each half of the pitch. The two views are overlapped and will be stitched to realize a unique view of the entire game field. Exploiting computer vision algorithms, salient areas to be zoomed and salient events to be included into the summarization clip will be selected.
In this project we study object appearance learning in the context temporally coherent visual data of lengthy video sequences (i.e. YouTube Videos). We focus on training an instance based object detector on unlabeled video data, using only the assumption that adjacent video frames contain semantically similar information. Learning is obtained using a local space-time condensing strategy which keeps the collected data sufficiently compact to remember all of the visual patterns that appeared so far.