Motion prediction for caching and prefetching in mouse-driven DVE navigation

Abstract

A distributed virtual environment (DVE) allows geographically separated users to participate in a shared virtual environment via connected networks. However, when the users are connected by the Internet, bandwidth limitation and network latency may seriously affect the performance and the interactivity of the system. This explains why there are very few DVE applications for the Internet. To address these shortcomings, caching and prefetching techniques are usually employed. Unfortunately, the effectiveness of these techniques depends largely on the accuracy of the prediction method used. Although there are a few methods proposed for predicting 3D motion, most of them are primarily designed for predicting the motion of specific objects by assuming certain object motion behaviors. We notice that in desktop DVE applications, such as virtual walkthrough and network gaming, the 2D mouse is still the most popular device used for navigation input. Through studying the motion behavior of a mouse during 3D navigation, we have developed a hybrid motion model for predicting the mouse motion during such navigation-a linear model for prediction at low-velocity motion and an elliptic model for prediction at high-velocity motion. The predicted mouse motion velocity is then mapped to the 3D environment for predicting the user's 3D motion. We describe how this prediction method can be integrated into the caching and prefetching mechanisms of our DVE prototype. We also demonstrate the effectiveness of the method and the resulting caching and prefetching mechanisms through extensive experiments.

Motion Prediction in Gesture-based Collaborative Design Environments

“…Each of these pulses represents a single movement step of the index finger -the start of the pulse represents the start of the finger motion and the end of the pulse represents the stop of the motion. If we normalize the pulses and plot their acceleration against velocity, we may observe an elliptic pattern as shown in Figure 3, which is similar to the observation made by [4] on human hand motion in manipulating a 2D mouse. Hence, we apply the hybrid elliptic motion model suggested by [4] here for hand motion prediction.…”

“…If we normalize the pulses and plot their acceleration against velocity, we may observe an elliptic pattern as shown in Figure 3, which is similar to the observation made by [4] on human hand motion in manipulating a 2D mouse. Hence, we apply the hybrid elliptic motion model suggested by [4] here for hand motion prediction. When a finger joint is at low velocity motion, we use a linear model for prediction.…”

“…We have conducted a thorough analysis of human finger motion and noticed that it has an elliptic motion behavior similar to our earlier observation [4] on human hand motion in manipulating a 2D mouse. We have also identified various finger motion constraints.…”

“…Hence, X is considered as the components of the state vector to the Kalman filter. The detail of this can be found in [4]. Hence, the predicted angle of a joint can be found by integrating ω t (X) in Eq.…”

“…In our situation, the network latency in CDE is typically high (0.2s or above). Hence, we need to anticipate for long prediction lengths and we focus on state transition density or motion modeling [4,13]. To improve the accuracy of the prediction, we also consider motion constraints.…”