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In order to make the algorithm robust to various challenging appearance changes, we adopt the powerful deep features in the description of tracking object appearance.
Furthermore, the existing trajectory tracking algorithms mainly deal with tracking object either using sophisticated invasive and expensive sensors [3,4], or treating tracking as a Hidden Markov Model (HMM) which needs adequate training data set to obtain model's parameter [5].
The presented approach has been applied to interesting open problems in computer vision such as shape analysis, image segmentation, tracking object deformations and object motion tracking, obtaining encouraging results and proving the effectiveness of the proposed approach.
Robust and effective solutions that could recover the information of tracking object are required for making wireless sensor networks more acceptable.
The techniques are applied for tracking object, event detection, context recognition, medical diagnosis and information management in military, academic, commercial, and industrial fields [14, 15].
The comparison between our method and the active contour method based on the moment demonstrates that our method is more effective in tracking object boundary edges used for video streams with a changing background.
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Two weeks after Chris first emerged from the coma, he began tracking objects with his eyes.
Depth data is additionally used for recognizing and tracking objects during the VR experience.
This paper describes a vision-based system for tracking objects from image sequences.
Sony has dabbled with the technology before, using two-dimensional barcodes known as CyberCodes as markers for tracking objects.
Accuracy of our methods in tracking objects is below 8 μm, in the order of more expensive commercial devices.
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