  State-of-the-art temporal action detectors to date are based on two-stream
input including RGB frames and optical flow. Although combining RGB frames and
optical flow boosts performance significantly, optical flow is a hand-designed
representation which not only requires heavy computation, but also makes it
methodologically unsatisfactory that two-stream methods are often not learned
end-to-end jointly with the flow. In this paper, we argue that optical flow is
dispensable in high-accuracy temporal action detection and image level data
augmentation (ILDA) is the key solution to avoid performance degradation when
optical flow is removed. To evaluate the effectiveness of ILDA, we design a
simple yet efficient one-stage temporal action detector based on single RGB
stream named DaoTAD. Our results show that when trained with ILDA, DaoTAD has
comparable accuracy with all existing state-of-the-art two-stream detectors
while surpassing the inference speed of previous methods by a large margin and
the inference speed is astounding 6668 fps on GeForce GTX 1080 Ti. Code is
available at \url{https://github.com/Media-Smart/vedatad}.

Computer Vision and Pattern Recognition;Artificial Intelligence