Abstract
Optical motion capture (MOCAP) is a commonly used technology to record the motion of non-rigid objects with high accuracy in 3D space. However, the MOCAP data has to be processed further before it can be used. The scattered reconstructed motion data must constitute a human configuration by labelling process according to the predefined template, and the missing markers have to be reconstructed to produce a stable motion trajectory. In this work, we propose a novel labelling method for motion sequences. First, a novel graph matching method is employed to determine the connection relationship of the scattered motion data for a single frame. Then, Kalman filtering is used for tracking in the motion sequence. As for the challenge coming from missing markers, we propose a new motion data preprocessing method considering the bone length constraint, which represents the information of variation in the relative position of adjacent markers. The processed motion data is input into a Long-Short Term Memory (LSTM) model to recover the missing markers and de-noise the motion data. The experiment conducted on our own dataset proves that our labelling method achieves a similar effect to Cortex, which is a commonly used commercial motion data analysis software. The experiment on CMU dataset demonstrates that our missing marker reconstruction method can achieve an art-of-state result. The labelling code will be pulished on https://github.com/Lijianfang6930/Graph-Matching-for-Marker-Labelling
Highlights
Motion capture is a technique which transforms human motion in the real world into a digital representation
While capturing the human motion, markers attached on the human body are recorded as 3D points in a real-world coordinate system for each timestamp and stored as digital representation [7]
We train a Long-Short Term Memory(LSTM) model using the new representation of motion data with bone constraint
Summary
Motion capture is a technique which transforms human motion in the real world into a digital representation. We first propose a novel graph match algorithm to label each marker trajectory with the help of manual initialization. Instead of using original 3D coordinates as human pose representation [18], a data preprocessing method is proposed to include the information of variation in the relative position of adjacent markers. We train a Long-Short Term Memory(LSTM) model using the new representation of motion data with bone constraint. We propose a graph retrieval method to match the predefined template of the human body for a single frame, Kalman filtering is employed as a tracking strategy for motion sequences. 2. We propose a data preprocessing method to generate a new representation of motion capture data with bone length constraint, which contains the information of variation in the relative position of adjacent markers. The experimental result shows the effectivity of our new motion data representation
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