  In this paper, we present a method for real-time multi-person human pose
estimation from video by utilizing convolutional neural networks. Our method is
aimed for use case specific applications, where good accuracy is essential and
variation of the background and poses is limited. This enables us to use a
generic network architecture, which is both accurate and fast. We divide the
problem into two phases: (1) pre-training and (2) finetuning. In pre-training,
the network is learned with highly diverse input data from publicly available
datasets, while in finetuning we train with application specific data, which we
record with Kinect. Our method differs from most of the state-of-the-art
methods in that we consider the whole system, including person detector, pose
estimator and an automatic way to record application specific training material
for finetuning. Our method is considerably faster than many of the
state-of-the-art methods. Our method can be thought of as a replacement for
Kinect, and it can be used for higher level tasks, such as gesture control,
games, person tracking, action recognition and action tracking. We achieved
accuracy of 96.8\%<EMAIL_ADDRESS>with application specific data.
