Continuous Deep Human Pose Estimation for Video Sequences

GLOBAL RESEARCH INSTITUTES

  • Continuous Deep Human Pose Estimation for Video Sequences

Continuous Deep Human Pose Estimation for Video Sequences

Principal Supervisor: Dr. Jesus Martinez del Rincon

Second Supervisor: Dr. Neil Robertson

+ Project Description

Human pose recovery is one of the most challenging research areas in computer vision. Successful estimations of the human pose provide information and simplify further tasks such as activity recognition or behaviour analysis, and therefore, it can benefit a wide range of industrial sectors such as video surveillance, physical security or sport performance enhancement. 

While constant steps forward have been made in this field, the 3D pose estimation from monocular images remains elusive. Recent advances in deep learning and their application to pose estimation have allowed to better reconstruct the 3D skeleton from static images. While impressive results have been achieved in certain images and poses, results tend to be inconsistent and inaccurate in upper and lower limbs. This is due to the inherent ambiguity of the 2D-to-3D projection and the presence of self-occlusions. As a result, when these techniques are applied to full video sequences the reconstructed skeletons do not keep continuity and suffer from jittering, making impossible to understand the performed motion or activity.

The aim of this project is to investigate different methods that allow introducing and exploiting the temporal information  within deep learning architecture for an enhance pose estimation in continuous vide sequences.

+ Objectives

  • · To develop new deep learning architectures for pose estimation in video sequences that allow feeding in and feeding back temporal information such as  the previous or the most likely poses. 
  • · To investigate the suitability of recurrent  layers such as LSTM to learn and preserve temporal pose sequentiality.
  • · To evaluate the performance of the developed methodologies against occlusions and the role of artificial data augmentation for improving robustness.

+ How to Apply

Applicants should apply electronically through the Queen’s online application portal at: https://dap.qub.ac.uk/portal/

+ Contact Details

Supervisor Name: Dr. Jesus Martinez del Rincon
Address:

Queens University of Belfast
School of EEECS
Computer Science Building,
18 Malone Road
BT7 1NN

Email:

j.martinez-del-rincon@qub.ac.uk

Tel:

+44 (0)28 90971779