Motion estimation is a crucial technique in computer vision and robotics that involves determining the movement of objects in a sequence of images or videos.
Motion estimation has seen significant advancements in recent years, thanks to the development of machine learning algorithms and deep learning techniques. Researchers have been exploring various approaches to improve the accuracy and efficiency of motion estimation, such as using auto-encoders, optical flow, and convolutional neural networks (CNNs). These methods have been applied to various applications, including human motion and pose estimation, cardiac motion estimation, and motion correction in medical imaging.
Recent research in the field has focused on developing novel techniques to address challenges in motion estimation. For example, the Motion Estimation via Variational Autoencoder (MEVA) method decomposes human motion into a smooth motion representation and a residual representation, resulting in more accurate 3D human pose and motion estimates. Another study proposed an Anatomy-Aware Tracker (AATracker) for cardiac motion estimation, which preserves anatomy by weak supervision and significantly improves tracking performance.
Practical applications of motion estimation include:
1. Human motion analysis: Accurate human motion estimation can be used in sports training, rehabilitation, and virtual reality applications to analyze and improve human movement.
2. Medical imaging: Motion estimation techniques can help improve the quality of medical images, such as MRI and PET scans, by correcting for motion artifacts and providing more accurate assessments of cardiac function.
3. Autonomous navigation: Motion estimation is essential for robots and autonomous vehicles to understand their environment and navigate safely.
A company case study in the field of motion estimation is Multimotion Visual Odometry (MVO), which estimates the full SE(3) trajectory of every motion in a scene, including sensor egomotion, without relying on appearance-based information. MVO has been applied to various multimotion estimation challenges and has demonstrated good estimation accuracy compared to similar approaches.
In conclusion, motion estimation is a vital technique in computer vision and robotics, with numerous practical applications. The advancements in machine learning and deep learning have significantly improved the accuracy and efficiency of motion estimation methods, paving the way for more sophisticated applications and solutions in the future.
Motion Estimation
Motion Estimation Further Reading
1.3D Human Motion Estimation via Motion Compression and Refinement http://arxiv.org/abs/2008.03789v2 Zhengyi Luo, S. Alireza Golestaneh, Kris M. Kitani2.Optical Flow-based 3D Human Motion Estimation from Monocular Video http://arxiv.org/abs/1703.00177v2 Thiemo Alldieck, Marc Kassubeck, Marcus Magnor3.Anatomy-Aware Cardiac Motion Estimation http://arxiv.org/abs/2008.07579v1 Pingjun Chen, Xiao Chen, Eric Z. Chen, Hanchao Yu, Terrence Chen, Shanhui Sun4.Shape-Adaptive Motion Estimation Algorithm for MPEG-4 Video Coding http://arxiv.org/abs/1002.1168v1 F. Benboubker, F. Abdi, A. Ahaitouf5.Motion Guided 3D Pose Estimation from Videos http://arxiv.org/abs/2004.13985v1 Jingbo Wang, Sijie Yan, Yuanjun Xiong, Dahua Lin6.Retrospective Motion Correction in Gradient Echo MRI by Explicit Motion Estimation Using Deep CNNs http://arxiv.org/abs/2303.17239v1 Mathias S. Feinler, Bernadette N. Hahn7.Learning-based and unrolled motion-compensated reconstruction for cardiac MR CINE imaging http://arxiv.org/abs/2209.03671v1 Jiazhen Pan, Daniel Rueckert, Thomas Küstner, Kerstin Hammernik8.Multimotion Visual Odometry (MVO) http://arxiv.org/abs/2110.15169v1 Kevin M. Judd, Jonathan D. Gammell9.Quasar Apparent Proper Motion Observed by Geodetic VLBI Networks http://arxiv.org/abs/astro-ph/0309826v1 D. S. MacMillan10.Motion correction for PET using subspace-based real-time MR imaging in simultaneous PET/MR http://arxiv.org/abs/2008.12359v2 Thibault Marin, Yanis Djebra, Paul K. Han, Yanis Chemli, Isabelle Bloch, Georges El Fakhri, Jinsong Ouyang, Yoann Petibon, Chao MaMotion Estimation Frequently Asked Questions
What do you mean by motion estimation?
Motion estimation is a technique used in computer vision and robotics to determine the movement of objects within a sequence of images or videos. It involves analyzing consecutive frames to identify and track the motion of objects, which can be used for various applications such as human motion analysis, medical imaging, and autonomous navigation.
Why is motion estimation used?
Motion estimation is used to analyze and understand the movement of objects in a scene, which is crucial for various applications. It can help improve the quality of medical images by correcting motion artifacts, assist in sports training and rehabilitation by analyzing human motion, and enable robots and autonomous vehicles to navigate safely by understanding their environment.
What is motion estimation and compensation?
Motion estimation is the process of determining the movement of objects within a sequence of images or videos. Motion compensation, on the other hand, is the process of using the estimated motion information to predict and correct for motion artifacts in the images or videos. Together, motion estimation and compensation can improve the quality of video compression, medical imaging, and other applications that require accurate motion information.
What are the methodologies in motion estimation?
There are several methodologies used in motion estimation, including: 1. Block matching: A technique that compares blocks of pixels in consecutive frames to estimate motion. 2. Optical flow: A method that computes the apparent motion of brightness patterns in an image sequence. 3. Feature-based methods: Techniques that track distinctive features in the image sequence to estimate motion. 4. Deep learning-based methods: Approaches that use machine learning algorithms, such as convolutional neural networks (CNNs) and auto-encoders, to learn and estimate motion patterns.
How has machine learning improved motion estimation?
Machine learning, particularly deep learning techniques, has significantly improved motion estimation by enabling more accurate and efficient methods. Researchers have developed various approaches, such as auto-encoders, optical flow, and convolutional neural networks (CNNs), which have been applied to various applications, including human motion and pose estimation, cardiac motion estimation, and motion correction in medical imaging.
What are some practical applications of motion estimation?
Practical applications of motion estimation include: 1. Human motion analysis: Used in sports training, rehabilitation, and virtual reality applications to analyze and improve human movement. 2. Medical imaging: Helps improve the quality of medical images, such as MRI and PET scans, by correcting for motion artifacts and providing more accurate assessments of cardiac function. 3. Autonomous navigation: Essential for robots and autonomous vehicles to understand their environment and navigate safely.
What are some recent advancements in motion estimation research?
Recent advancements in motion estimation research include the development of novel techniques to address challenges in the field. For example, the Motion Estimation via Variational Autoencoder (MEVA) method decomposes human motion into a smooth motion representation and a residual representation, resulting in more accurate 3D human pose and motion estimates. Another study proposed an Anatomy-Aware Tracker (AATracker) for cardiac motion estimation, which preserves anatomy by weak supervision and significantly improves tracking performance.
Can you provide a case study of a company using motion estimation?
A company case study in the field of motion estimation is Multimotion Visual Odometry (MVO), which estimates the full SE(3) trajectory of every motion in a scene, including sensor egomotion, without relying on appearance-based information. MVO has been applied to various multimotion estimation challenges and has demonstrated good estimation accuracy compared to similar approaches.
Explore More Machine Learning Terms & Concepts
