  Temporal Action Detection(TAD) is a crucial but challenging task in video
understanding.It is aimed at detecting both the type and start-end frame for
each action instance in a long, untrimmed video.Most current models adopt both
RGB and Optical-Flow streams for the TAD task. Thus, original RGB frames must
be converted manually into Optical-Flow frames with additional computation and
time cost, which is an obstacle to achieve real-time processing. At present,
many models adopt two-stage strategies, which would slow the inference speed
down and complicatedly tuning on proposals generating.By comparison, we propose
a one-stage anchor-free temporal localization method with RGB stream only, in
which a novel Newtonian Mechanics-MLP architecture is established. It has
comparable accuracy with all existing state-of-the-art models, while surpasses
the inference speed of these methods by a large margin. The typical inference
speed in this paper is astounding 4.44 video per second on THUMOS14. In
applications, because there is no need to convert optical flow, the inference
speed will be faster.It also proves that MLP has great potential in downstream
tasks such as TAD. The source code is available at
https://github.com/BonedDeng/TadML
