Beginning in the late 1980s, Aloimonos et al. introduced the first general framework for active vision in order to improve the perceptual quality of tracking results.
[3] Active control of the camera view point also helps in focusing computational resources on the relevant element of the scene.
It has also been suggested that visual attention and the selective aspect of active camera control can help in other tasks like learning more robust models of objects and environments with less labeled samples or autonomously .
In work from Denzler et al., the motion of a tracked object is modeled using a Kalman filter while the focal length that minimizes the uncertainty in the state estimations is the one that is used.
An attempt to join estimation and control in the same framework can be found in the work of Bagdanov et al., where a Pan-Tilt-Zoom camera is used to track faces.
[13] In a master/slave configuration, a supervising static camera is used to monitor a wide field of view and to track every moving target of interest.
[17] Applications include automatic surveillance, human robot interaction (video),[18][19] SLAM, route planning,[20] etc.
In the DARPA Grand Challenge most of the teams used LIDAR combined with active vision systems to guide driverless vehicles across an off-road course.
Controllable active vision methods described in the cited paper could help improve the process while relying on the human less.