Sentence examples for mean shift tracking from inspiring English sources

Beside that, standard mean shift tracking also tends to track only a part of the object.

Mean shift tracking discriminates between a target model in frame and a candidate model in frame.

Thus, the Auto GMM-SAMT tracking unit is much more robust than standard mean shift tracking.

In order to overcome the problem, by taking the merit from asymmetric kernel template, Liu et al. [12] propose an eigenshape kernel-based mean shift tracking algorithm to handle the scale changes of tracked objects.

Standard mean shift tracking is working with a symmetric kernel.

Table 3 Recall and Precision and measure of standard mean shift tracking and GMM-SAMT.

Object-based tracking methods such as mean-shift tracking [1] and particle filters [2] can in theory be used to obtain a background motion model, which can then be used to classify object tracks as foreground or background.

In the background anti-matching phrase, a certain number of background regions are extracted based on the feature of color orientation codes via an entropy filter, and the covariance matrix is adapted to match these regions to get the global motion of the background; further, the object matching is carried out by a mean-shift tracking algorithm.

Due to its ease of implementation, computational speed, and robust tracking performance, we decided to use a mean shift-based tracking algorithm [11], which belongs to the kernel tracking category.

On the one hand Auto GMM-SAMT takes adavantage of GMM-SAMT, which extends the standard mean shift algorithm to track the contour of objects of changing shape without the help of any predefined shape model.

Therefore, for the tracking unit of Auto GMM-SAMT we developed GMM-SAMT, a mean shift-based tracking method which is able to adapt to the object contour no matter what kind of 3D rotation the object is performing.