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We propose novel real-time algorithm to localize hands and find theirassociations with multiple people in the cluttered 4D volumetric data (dynamic3D volumes). Different from the traditional multiple view approaches, whichfind key points in 2D and then triangulate to recover the 3D locations, ourmethod directly processes the dynamic 3D data that involve both clutter andcrowd. The volumetric representation is more desirable than the partialobservations from different view points and enables more robust and accurateresults. However, due to the large amount of data in the volumetricrepresentation brute force 3D schemes are slow. In this paper, we propose novelreal-time methods to tackle the problem to achieve both higher accuracy andfaster speed than previous approaches. Our method detects the 3D bounding boxof each subject and localizes the hands of each person. We develop new 2Dfeatures for fast candidate proposals and optimize the trajectory linking usinga new max-covering bipartite matching formulation, which is critical for robustperformance. We propose a novel decomposition method to reduce the key pointlocalization in each person 3D volume to a sequence of efficient 2D problems.Our experiments show that the proposed method is faster than differentcompeting methods and it gives almost half the localization error.

Real-time Multiple People Hand Localization in 4D Point Clouds