  In this work we present a novel internal clock based space-time neural
network for motion speed recognition. The developed system has a spike train
encoder, a Spiking Neural Network (SNN) with internal clocking behaviors, a
pattern transformation block and a Network Dynamic Dependent Plasticity (NDDP)
learning block. The core principle is that the developed SNN will automatically
tune its network pattern frequency (internal clock frequency) to recognize
human motions in a speed domain. We employed both cartoons and real-world
videos as training benchmarks, results demonstrate that our system can not only
recognize motions with considerable speed differences (e.g. run, walk, jump,
wonder(think) and standstill), but also motions with subtle speed gaps such as
run and fast walk. The inference accuracy can be up to 83.3% (cartoon videos)
and 75% (real-world videos). Meanwhile, the system only requires six video
datasets in the learning stage and with up to 42 training trials. Hardware
performance estimation indicates that the training time is 0.84-4.35s and power
consumption is 33.26-201mW (based on an ARM Cortex M4 processor). Therefore,
our system takes unique learning advantages of the requirement of the small
dataset, quick learning and low power performance, which shows great potentials
for edge or scalable AI-based applications.
