Title: A New Representation of Skeleton Sequences for 3D Action Recognition,
Abstract: This paper presents a new method for 3D action recognition with skeleton
sequences (i.e., 3D trajectories of human skeleton joints). The proposed method
first transforms each skeleton sequence into three clips each consisting of
several frames for spatial temporal feature learning using deep neural
networks. Each clip is generated from one channel of the cylindrical
coordinates of the skeleton sequence. Each frame of the generated clips
represents the temporal information of the entire skeleton sequence, and
incorporates one particular spatial relationship between the joints. The entire
clips include multiple frames with different spatial relationships, which
provide useful spatial structural information of the human skeleton. We propose
to use deep convolutional neural networks to learn long-term temporal
information of the skeleton sequence from the frames of the generated clips,
and then use a Multi-Task Learning Network (MTLN) to jointly process all frames
of the generated clips in parallel to incorporate spatial structural
information for action recognition. Experimental results clearly show the
effectiveness of the proposed new representation and feature learning method
for 3D action recognition.