Bypass Enhancement RGB Stream Model for Pedestrian Action Recognition of Autonomous Vehicles
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Pedestrian action recognition and intention prediction are 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 improve the accuracy of the algorithm. However, there are relatively few studies and improvements in the computational complexity of algorithms and system real-time. In the autonomous driving application scenario, the real-time performance and ultra-low latency of the algorithm are extremely important evaluation indicators, which are directly re-lated to the availability and safety of the autonomous 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 training 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.
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