  A growing branch of computer vision is object detection. Object detection is
used in many applications such as industrial process, medical imaging analysis,
and autonomous vehicles. The ability to detect objects in videos is crucial.
Object detection systems are trained on large image datasets. For applications
such as autonomous vehicles, it is crucial that the object detection system can
identify objects through multiple frames in video. There are many problems with
applying these systems to video. Shadows or changes in brightness that can
cause the system to incorrectly identify objects frame to frame and cause an
unintended system response. There are many neural networks that have been used
for object detection and if there was a way of connecting objects between
frames then these problems could be eliminated. For these neural networks to
get better at identifying objects in video, they need to be re-trained. A
dataset must be created with images that represent consecutive video frames and
have matching ground-truth layers. A method is proposed that can generate these
datasets. The ground-truth layer contains only moving objects. To generate this
layer, FlowNet2-Pytorch was used to create the flow mask using the novel
Magnitude Method. As well, a segmentation mask will be generated using networks
such as Mask R-CNN or Refinenet. These segmentation masks will contain all
objects detected in a frame. By comparing this segmentation mask to the flow
mask ground-truth layer, a loss function is generated. This loss function can
be used to train a neural network to be better at making consistent predictions
on video. The system was tested on multiple video samples and a loss was
generated for each frame, proving the Magnitude Method's ability to be used to
train object detection neural networks in future work.
