  We propose a recurrent neural network-based spatio-temporal framework named
maskGRU for the detection and tracking of small objects in videos. While there
have been many developments in the area of object tracking in recent years,
tracking a small moving object amid other moving objects and actors (such as a
ball amid moving players in sports footage) continues to be a difficult task.
Existing spatio-temporal networks, such as convolutional Gated Recurrent Units
(convGRUs), are difficult to train and have trouble accurately tracking small
objects under such conditions. To overcome these difficulties, we developed the
maskGRU framework that uses a weighted sum of the internal hidden state
produced by a convGRU and a 3-channel mask of the tracked object's predicted
bounding box as the hidden state to be used at the next time step of the
underlying convGRU. We believe the technique of incorporating a mask into the
hidden state through a weighted sum has two benefits: controlling the effect of
exploding gradients and introducing an attention-like mechanism into the
network by indicating where in the previous video frame the object is located.
Our experiments show that maskGRU outperforms convGRU at tracking objects that
are small relative to the video resolution even in the presence of other moving
objects.
