1 research outputs found
Real-time Multiple People Hand Localization in 4D Point Clouds
We propose novel real-time algorithm to localize hands and find their
associations with multiple people in the cluttered 4D volumetric data (dynamic
3D volumes). Different from the traditional multiple view approaches, which
find key points in 2D and then triangulate to recover the 3D locations, our
method directly processes the dynamic 3D data that involve both clutter and
crowd. The volumetric representation is more desirable than the partial
observations from different view points and enables more robust and accurate
results. However, due to the large amount of data in the volumetric
representation brute force 3D schemes are slow. In this paper, we propose novel
real-time methods to tackle the problem to achieve both higher accuracy and
faster speed than previous approaches. Our method detects the 3D bounding box
of each subject and localizes the hands of each person. We develop new 2D
features for fast candidate proposals and optimize the trajectory linking using
a new max-covering bipartite matching formulation, which is critical for robust
performance. We propose a novel decomposition method to reduce the key point
localization in each person 3D volume to a sequence of efficient 2D problems.
Our experiments show that the proposed method is faster than different
competing methods and it gives almost half the localization error