CAMSHIFT IMPROVEMENT WITH MEAN-SHIFT SEGMENTATION, REGION GROWING, AND SURF METHOD
Abstract: CAMSHIFT algorithm
has been widely used in object tracking. CAMSHIFT utilizes color features as
the model object. Thus, original CAMSHIFT may fail when the object color issimilar
with the background color. In this study, we propose CAMSHIFT tracker combined
withmean-shift segmentation, region growing, and SURF in order to improve the
tracking accuracy. The mean-shift segmentation and region growing are applied
in object localization phase to extractthe important parts of the object. Hue-distance,
saturation, and value are used to calculate theBhattacharyya distance to judge
whether the tracked object is lost. Once the object is judged lost, SURF is
used to find the lost object, and CAMSHIFT can retrack the object. The Object
tracking system is built with OpenCV. Some measurements of accuracy have done
using frame-basedmetrics. We use datasets BoBoT (Bonn Benchmark on Tracking) to
measure accuracy of the system. The results demonstrate that CAMSHIFT combined
with mean-shift segmentation, region growing, and SURF method has higher
accuracy than the previous methods.
Penulis: Ferdinan; Yaya
Suryana
Journal Code: jptinformatikagg130014
