In this paper, we proposed an appearance-based tracking method for multiple fish tracking.
Secondly, we introduce a robust real-time 3D tracking method based on the Kalman filter for motion estimation and GNN for observation in order to track assignments.
The order tracking method based on time-frequency representation is regarded as an effective tool for fault detection of bearings with varying rotating speeds.
An adaptive direction-of-arrival (DOA) tracking method based upon a linear predictive model is developed.
According to the analysis of the control model for underwater robots, a path tracking method for underwater robots based on a line-of-sight method is proposed.
We use a volume of fluid (VOF) based interface tracking method in conjunction with a mass transfer model and a model for surface tension.
Among tracking methods based on features, algorithms based on color are able to provide a precise description of the object and track the object with high speed.
We then develop a theory, based on simplifying assumptions about the chaotic dynamics, to explain the success of the tracking method for chaotic systems.
Under the above conditions, Fig. 4 shows the average RMSE (ARMSE) of the multi-camera object tracking methods based on the CIF and EIF for different observation cluster sizes.
In recent years, order tracking methods based on time frequency representation have been proposed for bearing fault detection under speed variation operating conditions.
Fig. 4 Tracking accuracy of the multi-camera object tracking methods based on the CIF and EIF in terms of average RMSE for different cluster sizes.
