Binary Histogram based Split/Merge Object Detection using FPGAs

Tracking of objects using colour histograms has proven
successful in various visual surveillance systems. Such systems
rely heavily on similarity matrices to compare the appearance
of targets in successive frames. The computational
cost of the similarity matrix is increased if proximate
objects merge into a single object or a single object fragments
into two or more parts. This paper presents a method
of reducing this computational cost with the use of a reconfigurable
computing architecture. Colour histogram data of
moving targets are used to generate binary signatures for
the detection of merged or fragmented objects. The main
contribution in this paper is how binary histogram data is
generated and used to detect split/merge object with the use
of logical operations native to the hardware architecture
used for its implementation. The results show a 10 fold
improvement in processing speed over the microprocessor
based implementation, and that it is also capable of detecting
split/merge objects efficiently.