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The first algorithmic step within the Image
Analysis process was object detection. The object detection
algorithms were developed incrementally over the course of the
SUBITO project, with the performance of the algorithms tested at
each iteration using representative data collected from the test
site, and the results fed back in to the development process.
Initially a background and static object detection
algorithm was implemented, which detected static regions within
the scene, i.e. baggage hypotheses, which were validated against
various criteria to report potential abandoned baggage. The next
phase improved the robustness of the baggage verification and
developed a multi-view detection algorithm that would allow the
detected object to be positioned in 3D space. The final phase
implemented an enhanced baggage verification algorithm and the
shift of the algorithm from a purely Central Processing Unit
(CPU) based algorithm to an alternative Graphical Processing Unit
(GPU) based algorithm which provided a considerable speed improvement.
A number of component state-of-the-art algorithms
were used in the object detection to provide robust object
segmentation, specifically the detection of people and baggage
within the scene. These were designed to provide robust background
subtraction, static object detection and probable 3D location in
space, utilising adaptive processes to automatically adapt to the
current scene, and provide resilience against changes in the
image, e.g. lighting levels. A fusion of detection from multiple
views was also utilised to create a single world view of the likely
location of detected objects.
Person detection in multi-view scene
Baggage detection in multi-view scene
This multi-view approach reduced false alarms,
and produced robust detections of individuals and of baggage
as shown above.
The person and baggage detection algorithms
developed during the project achieved the aims of providing
robust localisation of moving objects alongside objects that have
become stationary. Improvements to the state-of-the-art component
algorithms, as shown at the final demonstration, have been
implemented while being able to maintain a relatively fast rate
(~6Hz) at which data could be output to the tracking module.
Further algorithmic improvements utilising Multi View-Infinite plane
techniques have increased the speed of operation to between 15 -
30 fps.
While the core object detection algorithmic
development, discussed above, focussed on the use of fixed CCTV
camera installations, an alternative approach was also considered
using PTZ cameras to achieve similar
goals of robust people and baggage detection.
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