  We present a 3D Convolutional Neural Networks (CNNs) based single shot
detector for spatial-temporal action detection tasks. Our model includes: (1)
two short-term appearance and motion streams, with single RGB and optical flow
image input separately, in order to capture the spatial and temporal
information for the current frame; (2) two long-term 3D ConvNet based stream,
working on sequences of continuous RGB and optical flow images to capture the
context from past frames. Our model achieves strong performance for action
detection in video and can be easily integrated into any current two-stream
action detection methods. We report a frame-mAP of 71.30% on the challenging
UCF101-24 actions dataset, achieving the state-of-the-art result of the
one-stage methods. To the best of our knowledge, our work is the first system
that combined 3D CNN and SSD in action detection tasks.
