  Temporal action detection (TAD) is a challenging task which aims to
temporally localize and recognize the human action in untrimmed videos. Current
mainstream one-stage TAD approaches localize and classify action proposals
relying on pre-defined anchors, where the location and scale for action
instances are set by designers. Obviously, such an anchor-based TAD method
limits its generalization capability and will lead to performance degradation
when videos contain rich action variation. In this study, we explore to remove
the requirement of pre-defined anchors for TAD methods. A novel TAD model
termed as Selective Receptive Field Network (SRF-Net) is developed, in which
the location offsets and classification scores at each temporal location can be
directly estimated in the feature map and SRF-Net is trained in an end-to-end
manner. Innovatively, a building block called Selective Receptive Field
Convolution (SRFC) is dedicatedly designed which is able to adaptively adjust
its receptive field size according to multiple scales of input information at
each temporal location in the feature map. Extensive experiments are conducted
on the THUMOS14 dataset, and superior results are reported comparing to
state-of-the-art TAD approaches.
