Object Recognition, Tracking and Trajectory Generation in Real-Time Video Sequence
Keywords:
Object tracking, tennis ball, segmentation, pattern recognition, and trajectory.Abstract
The aim of tracking is to detect objects through
images. This paper focuses on detection of moving objects in a
scene and tracking of the objects as long as they remain in
camera view. Initially noise removal and image enhancement is
carried out. The objects are detected from the supervised
Euclidean segmentation of the image frames. An object
recognition algorithm then classifies the segmented objects by
employing minimum distance classifiers approach, and by
comparing various length and shape descriptors. Finally,
object trajectory is generated based on centroid location of
geometric object. For the case study, we have considered
tracking of a tennis ball and shown its trajectory after tracking.
This method assures accurate image segmentation via
specifying color intensities of the object. Although median
based approach gives much better results but it’s rather much
more expensive. Same case is with Census Transform method
that is computationally expensive and is complex if multiple
objects are there per frame.
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