  Online temporal action localization from an untrimmed video stream is a
challenging problem in computer vision. It is challenging because of i) in an
untrimmed video stream, more than one action instance may appear, including
background scenes, and ii) in online settings, only past and current
information is available. Therefore, temporal priors, such as the average
action duration of training data, which have been exploited by previous action
detection methods, are not suitable for this task because of the high
intra-class variation in human actions. We propose a novel online action
detection framework that considers actions as a set of temporally ordered
subclasses and leverages a future frame generation network to cope with the
limited information issue associated with the problem outlined above.
Additionally, we augment our data by varying the lengths of videos to allow the
proposed method to learn about the high intra-class variation in human actions.
We evaluate our method using two benchmark datasets, THUMOS'14 and ActivityNet,
for an online temporal action localization scenario and demonstrate that the
performance is comparable to state-of-the-art methods that have been proposed
for offline settings.
