Abstract:
PTZ cameras can be an effective replacement for
multiple camera networks with their pan-tilt-zoom capability.
However, the state of the art scheduling method for the PTZ
cameras focuses mainly on tracking, not on coverage. In this
paper, we aim to maximize coverage as well as information gain,
thus, leading to effective surveillance. Towards this goal, we define
an information map that represents the sensitivity of a region.
We propose a scheduling algorithm in which the camera visits
those states more often that are likely to be more important
than others, thus, maximizing information gain. A probabilistic
framework is used to maximize information gain and coverage
simultaneously. Currently, there are no existing datasets and
methods to evaluate PTZ camera scheduling methods. We build
a real multi-camera dataset and develop a performance measure
for this purpose. Experimental results show that the proposed
stochastic scheduling algorithm based on adaptive information
gain probability is better than traditional as well as other variants
proposed in the paper in terms of information gain as well as
coverage.
