  Nowadays, skeleton information in videos plays an important role in
human-centric video analysis but effective coding such massive skeleton
information has never been addressed in previous work. In this paper, we make
the first attempt to solve this problem by proposing a multimodal skeleton
coding tool containing three different coding schemes, namely, spatial
differential-coding scheme, motionvector-based differential-coding scheme and
inter prediction scheme, thus utilizing both spatial and temporal redundancy to
losslessly compress skeleton data. More importantly, these schemes are switched
properly for different types of skeletons in video frames, hence achieving
further improvement of compression rate. Experimental results show that our
approach leads to 74.4% and 54.7% size reduction on our surveillance sequences
and overall test sequences respectively, which demonstrates the effectiveness
of our skeleton coding tool.
