  Pedestrian action recognition and intention prediction is one of the core
issues in the field of autonomous driving. In this research field, action
recognition is one of the key technologies. A large number of scholars have
done a lot of work to im-prove the accuracy of the algorithm for the task.
However, there are relatively few studies and improvements in the computational
complexity of algorithms and sys-tem real-time. In the autonomous driving
application scenario, the real-time per-formance and ultra-low latency of the
algorithm are extremely important evalua-tion indicators, which are directly
related to the availability and safety of the au-tonomous driving system. To
this end, we construct a bypass enhanced RGB flow model, which combines the
previous two-branch algorithm to extract RGB feature information and optical
flow feature information respectively. In the train-ing phase, the two branches
are merged by distillation method, and the bypass enhancement is combined in
the inference phase to ensure accuracy. The real-time behavior of the behavior
recognition algorithm is significantly improved on the premise that the
accuracy does not decrease. Experiments confirm the superiority and
effectiveness of our algorithm.
