  With a growing interest in autonomous vehicles' operation, there is an
equally increasing need for efficient anticipatory gesture recognition systems
for human-vehicle interaction. Existing gesture-recognition algorithms have
been primarily restricted to historical data. In this paper, we propose a novel
context and gap aware pose prediction framework(CGAP2), which predicts future
pose data for anticipatory recognition of gestures in an online fashion. CGAP2
implements an encoder-decoder architecture paired with a pose prediction module
to anticipate future frames followed by a shallow classifier. CGAP2 pose
prediction module uses 3D convolutional layers and depends on the number of
pose frames supplied, the time difference between each pose frame, and the
number of predicted pose frames. The performance of CGAP2 is evaluated on the
Human3.6M dataset with the MPJPE metric. For pose prediction of 15 frames in
advance, an error of 79.0mm is achieved. The pose prediction module consists of
only 26M parameters and can run at 50 FPS on the NVidia RTX Titan. Furthermore,
the ablation study indicates supplying higher context information to the pose
prediction module can be detrimental for anticipatory recognition. CGAP2 has a
1-second time advantage compared to other gesture recognition systems, which
can be crucial for autonomous vehicles.
