Real-time human pose estimation with convolutional neural networks
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Scientific › peer-review
Details
| Original language | English |
|---|---|
| Title of host publication | VISIGRAPP 2018 - Proceedings of the 13th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
| Publisher | SCITEPRESS |
| Pages | 335-342 |
| Number of pages | 8 |
| Volume | 5 |
| ISBN (Electronic) | 9789897582905 |
| DOIs | |
| Publication status | Published - 2018 |
| Publication type | A4 Article in a conference publication |
| Event | INTERNATIONAL CONFERENCE ON COMPUTER VISION THEORY AND APPLICATIONS - Duration: 1 Jan 1900 → … |
Conference
| Conference | INTERNATIONAL CONFERENCE ON COMPUTER VISION THEORY AND APPLICATIONS |
|---|---|
| Period | 1/01/00 → … |
Abstract
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 in restricted environments. It can be used for 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.
ASJC Scopus subject areas
Keywords
- Convolutional neural networks, Human pose estimation, Person detection