We present an example-based approach to pose recovery, using histograms of oriented gradients as image descriptors. Tests on the HumanEva-I and HumanEva-II data sets provide us insight into the strengths and limitations of an example-based approach. We report mean relative 3D errors of approximately 65 mm per joint on HumanEva-I, and 175 mm on HumanEva-II. We discuss our results using single and multiple views. Also, we perform experiments to assess the algorithm’s generalization to unseen subjects, actions and viewpoints. We plan to incorporate the temporal aspect of human motion analysis to reduce orientation ambiguities, and increase the pose recovery accuracy.
|Name||CTIT Technical Report Series|
|Publisher||University of Twente, Centre for Telematics and Information Technology|
- EC Grant Agreement nr.: FP6/033812
- HMI-CI: Computational Intelligence
- HMI-HF: Human Factors
- HMI-VRG: Virtual Reality and Graphics