  The ability to estimate College Campus Occupancy for Classrooms and Labs in
real time has become one of the major concerns for various Academicians,
authorities and administrators,where still a manual attendance marking system
is being followed. Using a low budget multiple sensor setup installed in a
college auditorium, the goal is to build a real-time occupancy detector. This
paper presents an Inductive real time Decision tree based classifier using
multiple sensor dataset to detect occupancy. Using simple feature based
thresholds, Reverberation time which comes out to be a novel as well as most
distinguishing feature sampled at various frequencies over a given time
interval was used to detect the occupancy with an accuracy of %.Addition of
various other sensor data, decreased the accuracy of classification results.
The detector setup can be used in various college buildings to provide real
time centralised occupancy status thus automating the manual attendance system
being used.
