A common goal in many vision applications is to
identify and track human with distinctive visual
features or “tags”. Examples range from identifying
numbers on sports jersey to locating faces in crowded
environment. In this project, we propose a general
framework for camera placement that is optimal for
this "visual tagging problem". The optimal placement is
computed by capturing the vision requirements via a
set of binary variables over the environmental grid
points and solving it using binary integer
The GUI on top allows the user to input the environment with possible obstacles (black box),
computes the optimal camera placement with 12 cameras (blue arrows) and simulates its
coverage (sub-image on the right with brigter grayscale indicating better coverage.) Simulation
results below show the red tag on the random humanoid in blue shirt to be visible in camera 1,
2, 11 and 12.
To proble further, see our publication page and demo page.