Predictive tracking: Difference between revisions - VR & AR Wiki - Virtual Reality & Augmented Reality Wiki

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==Introduction==

[[Predictive tracking]] is a fundamental technique used in both [[augmented reality]] (AR) and [[virtual reality]] (VR) systems that anticipates where a user's body parts or viewing direction will be in the near future. This computational method works by analyzing current motion patterns, velocity, and acceleration to estimate future positions before they occur<ref name="LaValle2016"></ref>. For example, when a VR game needs to display your virtual hand's position, it doesn't simply render where your hand currently ~~is—it~~ predicts where your hand will be several milliseconds in the future.

[[Predictive tracking]] is a fundamental technique used in both [[augmented reality]] (AR) and [[virtual reality]] (VR) systems that anticipates where a user's body parts or viewing direction will be in the near future. This computational method works by analyzing current motion patterns, velocity, and acceleration to estimate future positions before they occur<ref name="LaValle2016"></ref>. For example, when a VR game needs to display your virtual hand's position, it doesn't simply render where your hand currently is, it predicts where your hand will be several milliseconds in the future.

The primary purpose of predictive tracking is to combat [[latency]] issues inherent in AR and VR systems. Without predictive algorithms, users would experience a noticeable delay between their physical movements and the corresponding visual feedback on their displays. This delay creates a disconnection that not only diminishes the sense of [[immersion]] but can also contribute to [[motion sickness]] and general discomfort<ref name="Abrash2014"></ref>. Through predictive tracking, the system estimates your future orientation and position based on your current input data, significantly reducing perceived latency and creating a more natural and responsive experience.

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==History and Development==

The concept of predictive tracking has roots in early [[computer vision]] and [[human-computer interaction]] research dating back to the 1990s. However, its critical importance for immersive technologies became apparent with the resurgence of consumer VR in the early 2010s~~<ref name="Oculus2013"></ref>~~. Early VR prototypes suffered from significant motion-to-photon latency issues, making predictive algorithms essential for creating viable consumer products.

The concept of predictive tracking has roots in early [[computer vision]] and [[human-computer interaction]] research dating back to the 1990s. However, its critical importance for immersive technologies became apparent with the resurgence of consumer VR in the early 2010s. Early VR prototypes suffered from significant motion-to-photon latency issues, making predictive algorithms essential for creating viable consumer products.

[[John Carmack]], while working as CTO at Oculus, popularized the implementation of predictive tracking algorithms in consumer VR and emphasized their importance in reducing perceived latency. His work on "timewarp," a rendering technique that incorporates prediction to update images just before display, became fundamental to modern VR systems<ref name="Carmack2013"></ref>.

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==Latency Sources==

Understanding the sources of latency in AR and VR systems is crucial to implementing effective predictive tracking solutions. A specialized device known as a [[latency tester]] measures "motion-to-photon" latency within a ~~headset—the~~ time delay between physical movement and the corresponding visual update on the display. The longer this delay, the more uncomfortable and less immersive the experience becomes.

Understanding the sources of latency in AR and VR systems is crucial to implementing effective predictive tracking solutions. A specialized device known as a [[latency tester]] measures "motion-to-photon" latency within a headset, the time delay between physical movement and the corresponding visual update on the display. The longer this delay, the more uncomfortable and less immersive the experience becomes.

Several distinct factors contribute to the overall system latency:

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*'''Double Exponential Smoothing''' - This statistical technique gives more weight to recent observations while still considering historical data. It's particularly effective for tracking movements with gradual acceleration or deceleration patterns, such as head rotations that naturally speed up and slow down<ref name="LaViola2003"></ref>.

*'''Artificial Neural Networks''' - Modern AR and VR systems increasingly incorporate machine learning approaches to prediction. Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks can learn complex patterns in human movement, potentially outperforming traditional algorithms for users with consistent movement styles. These approaches require training data but can adapt to individual users over time~~<ref name="Orozco2019"></ref>~~.

*'''Artificial Neural Networks''' - Modern AR and VR systems increasingly incorporate machine learning approaches to prediction. Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) networks can learn complex patterns in human movement, potentially outperforming traditional algorithms for users with consistent movement styles. These approaches require training data but can adapt to individual users over time.

*'''Hybrid Approaches''' - State-of-the-art predictive tracking often combines multiple algorithms, using fast methods for immediate response and more sophisticated algorithms to refine predictions. For example, a system might use dead reckoning for immediate feedback while a Kalman filter computes a more accurate prediction in parallel<ref name="Greer2020"></ref>.

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*'''Dynamic Resolution Scaling''' - To maintain frame rates critical for effective predictive tracking, many systems dynamically adjust rendering resolution based on scene complexity and current performance metrics. This technique ensures consistent frame timing, which is essential for predictive algorithms that depend on regular update intervals<ref name="Patney2016"></ref>.

*'''Sensor Fusion''' - Before prediction occurs, raw sensor data must be combined through sensor fusion techniques. These approaches merge data from complementary sensors (~~e.g.,~~ combining gyroscope data with camera-based tracking) to create a more accurate representation of current position and orientation. The quality of this fusion directly impacts prediction accuracy<ref name="Foxlin1996"></ref>.

*'''Sensor Fusion''' - Before prediction occurs, raw sensor data must be combined through sensor fusion techniques. These approaches merge data from complementary sensors (for example combining gyroscope data with camera-based tracking) to create a more accurate representation of current position and orientation. The quality of this fusion directly impacts prediction accuracy<ref name="Foxlin1996"></ref>.

*'''Simultaneous Localization and Mapping (SLAM)''' - In AR systems and inside-out tracking VR headsets, SLAM techniques construct and maintain maps of the surrounding environment. While SLAM primarily focuses on determining current position rather than predicting future positions, these maps provide valuable contextual information that can constrain and improve predictions<ref name="Davison2007"></ref>.

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<ref name="Abrash2014">Abrash, M. (2014). "What VR Could, Should, and Almost Certainly Will Be Within Two Years." Steam Dev Days, Seattle.</ref>

<ref name="Azuma1997">Azuma, R. T. (1997). "A Survey of Augmented Reality." Presence: Teleoperators and Virtual Environments, 6(4), pp. 355-385.</ref>

~~<ref name="Oculus2013">Oculus VR (2013). "Measuring Latency in Virtual Reality Systems." Oculus Developer Documentation.</ref>~~

<ref name="Carmack2013">Carmack, J. (2013). "Latency Mitigation Strategies." Oculus Connect Keynote.</ref>

<ref name="Yao2014">Yao, R., Heath, T., Davies, A., Forsyth, T., Mitchell, N., & Hoberman, P. (2014). "Oculus VR Best Practices Guide." Oculus VR.</ref>

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<ref name="Welch2002">Welch, G., & Foxlin, E. (2002). "Motion Tracking: No Silver Bullet, but a Respectable Arsenal." IEEE Computer Graphics and Applications, 22(6), pp. 24-38.</ref>

<ref name="Welch2006">Welch, G., & Bishop, G. (2006). "An Introduction to the Kalman Filter." University of North Carolina at Chapel Hill, Department of Computer Science, Technical Report 95-041.</ref>

<ref name="Isard1998">Isard, M., & Blake, A. (1998). "~~CONDENSATION—Conditional~~ Density Propagation for Visual Tracking." International Journal of Computer Vision, 29(1), pp. 5-28.</ref>

<ref name="Isard1998">Isard, M., & Blake, A. (1998). "CONDENSATION-Conditional Density Propagation for Visual Tracking." International Journal of Computer Vision, 29(1), pp. 5-28.</ref>

<ref name="LaViola2003">LaViola, J. J. (2003). "Double Exponential Smoothing: An Alternative to Kalman Filter-Based Predictive Tracking." Proceedings of the Workshop on Virtual Environments, pp. 199-206.</ref>

~~<ref name="Orozco2019">Orozco Gómez, D., & Malkani, A. (2019). "Deep Learning for Movement Prediction in Mixed Reality." Microsoft Research Technical Report.</ref>~~

<ref name="Greer2020">Greer, J., & Johnson, K. (2020). "Multi-modal Prediction for XR Tracking." IEEE Conference on Virtual Reality and 3D User Interfaces (VR), pp. 161-170.</ref>

<ref name="Olsson2011">Olsson, T., & Salo, M. (2011). "Narratives of Satisfying and Unsatisfying Experiences of Current Mobile Augmented Reality Applications." Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 2779-2788.</ref>

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</references>

[[Category:Terms]] [[Category:Technical Terms]]

[[Category:Terms]]

[[Category:Technical Terms]]

[[Category:Tracking]]

[[Category:Tracking Types]]