  Temporal action recognition always depends on temporal action proposal
generation to hypothesize actions and algorithms usually need to process very
long video sequences and output the starting and ending times of each potential
action in each video suffering from high computation cost. To address this,
based on boundary sensitive network we propose a new temporal convolution
network called Multipath Temporal ConvNet (MTN), which consists of two parts
i.e. Multipath DenseNet and SE-ConvNet. In this work, one novel high
performance ring parallel architecture based on Message Passing Interface (MPI)
is further introduced into temporal action proposal generation, which is a
reliable communication protocol, in order to respond to the requirements of
large memory occupation and a large number of videos. Remarkably, the total
data transmission is reduced by adding a connection between multiple computing
load in the newly developed architecture. It is found that, compared to the
traditional Parameter Server architecture, our parallel architecture has higher
efficiency on temporal action detection task with multiple GPUs, which is
suitable for dealing with the tasks of temporal action proposal generation,
especially for large datasets of millions of videos. We conduct experiments on
ActivityNet-1.3 and THUMOS14, where our method outperforms other state-of-art
temporal action detection methods with high recall and high temporal precision.
In addition, a time metric is further proposed here to evaluate the speed
performance in the distributed training process.
