Multiple cameras are
needed to cover large environments for monitoring activity. To track people
successfully in multiple perspective imagery, one needs to establish correspondence
between objects captured in multiple cameras. We present a system for
tracking people in multiple uncalibrated cameras. The system is able to
discover spatial relationships between the camera fields of view and use
this information to correspond between different perspective views of
the same person. We employ the novel approach of finding the limits of
field of view (FOV) of a camera as visible in the other cameras. Using
this information, when a person is seen in one camera, we are able to
predict all the other cameras in which this person will be visible. Moreover,
we apply the FOV constraint to disambiguate between possible candidates
of correspondence. Tracking in each individual camera needs to be resolved
before such an analysis can be applied. We perform tracking in a single
camera using background subtraction, followed by region correspondence.
This takes into account the velocities, sizes and distance of bounding
boxes obtained through connected component labeling. We present results
on sequences taken from the PETS 2001 dataset, which contain several persons
and vehicles simultaneously. The proposed approach is very fast compared
to camera calibration based approaches.
Sequence 1: [Training
Sequence, used to generate lines] [results]
[symbolic results]
Sequence 2:
[results] [symbolic
results]
Sequence 3: [orig
(small sample)] [symbolic results] Presentation
given at HUMO 2000 (Dec 2000) |