Sentence examples for estimating object from inspiring English sources

In this work, we introduce two intuitive and general metrics to allow for objective comparison of tracker characteristics, focusing on their precision in estimating object locations, their accuracy in recognizing object configurations and their ability to consistently label objects over time.

Simulations demonstrate that this method requires large blocks of data and thus may be appropriate for estimating object displacement in background noise.

This paper also presents the results of a study of the detection sensitivity of the LMT system as well as a new measurement-based model for estimating object size from LMT measurements.

In addition, no significant downward shift of CA estimates was found when upright (t(14) = 1.5, p = .15) relative to the ground truth prediction, which would have indicated being conservative in estimating object stability.

This observation is based on an object-based quantitative approach that records three different parameters describing the accuracy of estimating object volume without visual inspection, and thereby the perception of joint positions in the hand.

While computing object-based spatial frequencies is relatively straightforward with stationary objects, such as static images of human faces, one challenge in estimating object-based spatial frequencies in the current study is due to variability in body size and body postures over time in the action videos.

To act intelligently in dynamic environments, mobile robots must estimate object positions using information obtained from a variety of sources.

Many works in multi-object tracking have proposed some partial solution [10 13] using sequential or global strategies to estimate object trajectories and overcome identification errors.

An additional problem is that the accuracy of the estimated object trajectories can be very poor due to the high dimensionality of the tracking problem.

On the other hand, a Rao-Blackwellization technique has been used to improve the accuracy of the estimated object trajectories, which is a fundamental aspect in the tracking of multiple objects due to its high dimensionality.

In the context of tracking, a hypothesis corresponds to a set of mobile objects representing a possible configuration, given previously estimated object attributes (e.g. width, length, velocity) and new incoming visual evidence (blobs at current frame).