Recently, several approaches have been introduced for incorporating the information from multiple cameras to increase the robustness of tracking. This allows to handle problems of mutually occluding objects - a reasonable scenario for many tasks such as visual surveillance or sports analysis. However, these methods often ignore problems such as inaccurate geometric constraints and violated geometric assumptions, requiring complex methods to resolve the resulting errors. In this paper, we introduce a new multiple camera tracking approach that inherently avoids these problems. We build on the ideas of generalized Hough voting and extend it to the multiple camera domain. This offers the following advantages: we reduce the amount of data in voting and are robust to projection errors. Moreover, we show that using additional geometric information can help to train more specific classifiers drastically improving the tracking performance. We confirm these findings by comparing our approach to existing (multi-camera) tracking methods.
To increase the robustness of detection in intelligent video surveillance systems, homography has been widely used to fuse foreground regions projected from multiple camera views to a reference view. However, the intersections of non-corresponding foreground regions can cause phantoms. This paper proposes an algorithm based on geometry and colour cues to cope with this problem, in which the homography between different camera views and the Mahalanobis distance between the colour distributions of every two associated foreground regions are considered. The integration of these two matching algorithms improves the robustness of the pedestrian and phantom classification. Experiments on real-world video sequences have shown the robustness of this algorithm.
Monitoring a ground source heat pump can provide important insights into its working, but to study the behaviour of the borehole heat exchanger (BHE) we require monitored data for the whole period ...