We present a probabilistic framework for component-based automatic detection and tracking of objects in video. We represent objects as spatio-temporal two-layer graphical models, w...
Leonid Sigal, Ying Zhu, Dorin Comaniciu, Michael J...
Background subtraction algorithms define the background
as parts of a scene that are at rest. Traditionally,
these algorithms assume a stationary camera, and identify
moving obj...
Abstract--This work is dedicated to a statistical trajectorybased approach addressing two issues related to dynamic video content understanding: recognition of events and detection...
Alexandre Hervieu, Patrick Bouthemy, Jean-Pierre L...
This paper describes a method for separating moving objects from temporally varying background in time-lapse confocal microscopy image sequences representing fluorescently tagged ...
We present an approach for persistent tracking of moving objects observed by non-overlapping and moving cameras. Our approach robustly recovers the geometry of non-overlapping vie...