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Embedded tracking is implemented.
Between the gorgeous photos and embedded tracks, this piece really transports you.
In Section 3, we show the embedded tracking algorithm for visual object compression.
A study conducted by the researchers at Oxford University finds that companies can identify and track users across multiple platforms using embedded tracking features in third-party mobile apps.
Compared with the target object tracking algorithm, the embedded tracking algorithm based on visual object compression has a significant advantage in the reconstruction of the moving target.
Based on the results of the above researches, the embedded tracking algorithm based on multi-feature crowd fusion and visual object compression was proposed for mobile object tracking.
In order to improve the positioning accuracy and the quality of tracking service, we propose an embedded tracking algorithm based on multi-feature fusion and visual object compression.
To sum up, the embedded tracking algorithm based on multi-feature fusion and localization of visual target compression is shown in Fig. 4.
In order to improve the positioning accuracy and improve the quality of tracking service, we propose an embedded tracking algorithm based on multi-feature crowd fusion and visual object compression.
The weather is cloudy, the sun is not enough, the embedded tracking algorithm based on multi-feature fusion and visual target compression is recorded as ET-MVC, and the target object feature tracking algorithm is denoted as T-OTF.
On the other hand, to reduce the dimension of the multidimensional space composed of the moving object visual frame and the compression of the visual object, the embedded tracking algorithm is established.
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