Publication: Visual feature tracking based on PHD filter for vehicle detection
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2014-10-07
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Tutors
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IEEE
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Abstract
Vehicle detection is one of the classical application
among the Advance Driver Assistance Systems (ADAS).
Applications like emergency braking or adaptive cruise control
(ACC) require accurate and reliable vehicle detection. In latest
years the improvements in vision detection have lead to the
introduction of computer vision to detect vehicles by means of
these more economical sensors, with high reliability.
In the present paper, a novel algorithm for vehicle detection
and tracking based on a probability hypothesis density (PHD)
filter is presented. The first detection is based on a fast machine
learning algorithm (Adaboost) and Haar-Like features. Later,
the tracking is performed, by means features detected within the
bounding box provided by the vehicle detection. The features, are
tracked by a PHD filter. The results of the features being tracked
are combined together in the last step, based on several different
methods. Test provided show the performance of the PHD filter
in public sequences using the different methods proposed.
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Keywords
PHD Filter, Vehicle detection, Computer vision, Intelligent Transport Systems
Bibliographic citation
Information Fusion (FUSION), 2014 17th International Conference on. IEEE, pp. 1-6