Download now Free registration required
The use of autonomous pan-tilt cameras as opposed to static cameras can dramatically enhance the range and effectiveness of surveillance systems, but effective tracking in such pan-tilt scenarios remains a challenge. Existing approaches for constructing mosaiced background models require accurate camera motion parameters, and online updates for the background model in the presence of scene activity, as well as real-time tracking of targets in the presence of partial occlusions have not been solved. This paper proposes a model that requires no camera motion parameters, the background is learned online, and the solution is integrated with target tracking. Camera egomotion is estimated as the dominant cluster mean for a mixture of Gaussians learned over point correlations between consecutive frames.
- Format: PDF
- Size: 419 KB