… problem of online action detection. Unlike traditional action recognition and action detection
as … of online action detection is to detect an action as it happens and ideally even before the …

Most recent approaches for online action detection tend to apply Recurrent Neural Network (RNN)
to capture long-range temporal structure. However, RNN suffers from non-parallelism …

… to this existing online action detection work … online action detection and anticipation of the
immediate future, and uses this estimated “future information” to improve the action detection …

… LSTR), a temporal modeling algorithm for online action detection, which employs a long- and
short… LSTR achieves state-of-the-art performance on three standard online action detection …

… background and irrelevant actions as well as the action of interest. For online action detection,
in … discriminate the information relevant to an ongoing action from others. Our unit, named …

… Online action detection goes one step further and is more challenging, which identifies the
action type and localizes the action … the problem of online action detection from streaming …

… The primary difference between online action detection and offline action detection
algorithms lies in whether future frames can be accessed. In offline algorithms, Xu et al. [44] …

… Fortunately in real-life, human actions are not independent from … online action detection
method should be able to capture these dependencies, to enable a more accurate early detection…

… of regression provides the ability to forecast the action prior to its occurrence. We … in action
detection and forecasting on the Online Action Detection Dataset (OAD) and Gaming Action …

… in online action detection to put separate emphasis on low-confident hard action and back…
We present GateHUB for online action detection in untrimmed streaming videos. It consists of …