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Evaluation of Multi-part Models for Mean-Shift Tracking



Institution: IEEE
Subject Keywords: Computer Science;

Mean-shift tracking is a data-driven technique for tracking
objects through a video sequence. We propose an innovation
to mean-shift tracking that combines the background
exclusion constraint with multi-part appearance models.
The former constraint prevents the tracker from moving to
regions where no foreground objects are present, while the
multi-part nature of the models enforces a spatial structure
on the tracked object. We also use a simple formula to determine
the scale of the object in each video frame, and note
the importance of setting an appropriate convergence condition.
An evaluation of our proposed tracker and several
existing trackers is performed using a ground truth dataset.
We demonstrate that our innovation yields more accurate
tracking than existing mean-shift techniques.

Suggested citation:

DAWSON-HOWE, KENNETH MARK; . () Evaluation of Multi-part Models for Mean-Shift Tracking [Online]. Available from: [Accessed: 12th November 2019].


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