Real-time Human Pose Estimation from Video with Convolutional Neural Networks
It provides a faster alternative to Kinect for domain-specific applications like gesture control and games, though it is incremental in approach.
The paper tackles real-time multi-person human pose estimation from video using convolutional neural networks, achieving 96.8% accuracy (PCK@0.2) with application-specific data.
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\% (PCK@0.2) with application specific data.