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Several researchers have explored unsupervised methods for motion analysis.
Wearable sensors and isomorphic vision sensors are the most used sensing methods for motion acquisition.
During the last decade, sampling based methods for motion and path planning have gained more interest.
This paper is a survey of existing methods for motion prediction and risk assessment for intelligent vehicles.
In recent years, some of the most convincing experimental results have been obtained using promising methods for motion planning.
Current compression methods for motion capture data are primarily lossy and cause distortions in the motion data.
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First, we tested the processing methods for motions with only one constraint.
We propose a joint optical flow and principal component analysis (PCA) method for motion detection.
The isometric vision sensor-based sensing is the non-intrusive method for motion acquisition.
We present a novel method for motion segmentation by learning the motion priors from exemplar motions to guide the segmentation.
Taking into account the aforementioned considerations, a new method for motion information processing is proposed in this section.
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