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...
We present a novel framework for tracking of a long sequence of human activities, including the time instances of change from one activity to the next, using a closed-loop, non-li...
In this paper we investigated the use of Genetic Programming (GP) to evolve programs which could detect moving objects in videos. Two main approaches under the paradigm were propo...
This paper demonstrates the use of a single-chip FPGA for the segmentation of moving objects in a video sequence. The system maintains highly accurate background models, and integ...
Kofi Appiah, Andrew Hunter, Patrick Dickinson, Hon...
3D scene understanding is key for the success of applications such as autonomous driving and robot navigation. However, existing approaches either produce a mild level of understa...