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Usually multiple camera networks are employed to cover, monitor and improve the safety of people in large multifunctional crowded buildings.
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On the other hand, a multiple camera network allows to view the scene from many directions, monitoring an area larger than what a single stereo sensor can do.
Real world camera networks often span multiple viewpoints within a facility, including many regions of overlap.
In multi-camera networks, multiple cameras have an overlapping FOV and thus can observe an object simultaneously.
While the integration of multiple cameras into a network has the potential advantage of fusing complementary observations from sensors and enlarging visual coverage, it also increases the complexity of tracking tasks and poses challenges to system scalability.
In many real-world camera networks, there are often several links due to multiple directly connected cameras; in this case, their estimated CLM will decrease the accuracy due to higher outlier percentage.
Similarly, multiple images of the same subjects under varying viewpoints are also available in video camera networks.
The system is called Equal Baseline Multiple Camera Set (EBMCS).
Camera networks are widely used for security and tracking.
In recent years, camera networks have changed dramatically.
Additionally, we use an extensive camera-trap data set to evaluate independence between multiple camera traps per SU.
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