Abstract
Human motion capture by optical sensors produces snapshots of the motion of a cloud of points that need to be labeled in order to carry out ensuing motion analysis for medical or other purposes. We generate the labeling of instantaneous captures of the cloud of points, discarding temporal correlations, in the presence of occlusions. Our approach proposes an ensemble of weak classifiers defined over geometrical features extracted from small subsets of the cloud of points. We apply an Adaboost strategy to select a minimal ensemble of weak classifiers achieving a target correct labeling detection accuracy. Furthermore, we use these features to generate the labeling of the points in the cloud even in the presence of occlusions.To deal with the occlusions of markers we search for ensembles of partial labeling solvers which can provide partial consistent labelings which cover the unoccluded markers. We test two greedy search approaches and a genetic algorithm in the search for the optimal ensemble of partial solvers We demonstrate the approach on a real dataset obtained from the measurement of gait motion of persons, with available ground truth labeling. Results are encouraging, achieving high accuracy label generation at a reduced computational cost.
Highlights
Motion capture (MoCap) is the process of object motion quantification in order to build computer models that allow further fine analysis
Human motion capture is often needed for clinical purposes, such as gait analysis [28], but there are emerging applications such as human-robot collaboration [14], where human motion prediction is critical for the safe interaction of humans and robots in the workplace
Following an Adaboost [29] approach, we define a weak classifier from each feature as as follows: each feature falls within a range of values [α, β] when the labeling of is correct, a weak classifier checks if the feature value falls within the specified interval, i.e
Summary
Motion capture (MoCap) is the process of object motion quantification in order to build computer models that allow further fine analysis. We propose a robust approach which recovers from occlusions, labeling all the candidate points at each frame. Matching subset: (a) a genetic algorithm, (b) a tree-search approach that computes the minimal set of weak classifiers achieving the maximal labeling accuracy, which is the greatest subset of model marker points labeled with precision high enough. We achieve an optimal balance between the number of times that the overall algorithm succeeds to establish the right labelings, called hit rate, and the number of times the process declares a marker as occluded when it isn’t, the false occlusions rate.
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