Robust arbitrary view gait recognition based on parametric 3D human body reconstruction and virtual posture synthesis
By Jian Luo, Jin Tang, Tardi Tjahjadi, Xiaoming Xiao. 2016
51 citations recorded by the source corpus.
This paper proposes an arbitrary view gait recognition method where the gait recognition is performed in 3-dimensional (3D) to be robust to variation in speed, inclined plane and clothing, and in the presence of a carried item. 3D parametric gait models in a gait period are reconstructed by an optimized 3D human pose, shape and simulated clothes estimation method using multi-view gait silhouettes. The gait estimation involves morphing a new subject with constant semantic constraints using silhouette cost function as observations. Using a clothes-independent 3D parametric gait model reconstruction method, gait models of different subjects with various postures in a cycle are obtained and used as galleries to construct 3D gait dictionary. Using a carrying-items posture synthesized model, virtual gait models with different carrying-items postures are synthesized to further construct an over-complete 3D gait dictionary. A self-occlusion optimized simultaneous sparse representation model is also introduced to achieve high robustness in limited gait frames. Experimental analyses on CASIA B dataset and CMU MoBo dataset show a significant performance gain in terms of accuracy and robustness. Introduces a clothes-independent 3D human pose and shape estimation method.Introduces a carried-item posture synthesized model for 3D gait recognition.Discusses a self-occlusion optimized gait simultaneous sparse representation model.
Subfields: Biomedical Engineering, Computer Vision and Pattern Recognition, Human-Computer Interaction
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Biomedical Engineering
In Computer Vision and Pattern Recognition
- Cynthia Rudin
- Chaofan Tao
- Joseph Y. Lo
- Alina Jade Barnett
- Fides Regina Schwartz
- Yinhao Ren
- Christin Seifert
- Meike Nauta
In Human-Computer Interaction
Citations in the corpus
- A case-based interpretable deep learning model for classification of mass lesions in digital mammography (2021, background): “Ways to direct model attention include data augmentation [16, 33, 8], techniques that combat hand-selected confounders [51, 50], techniques that combat learned confounders [58], and an approach where a human critic manually approves the attention map during training [43].”
- Pattern Recognition (2018, unknown): “Recent gait recognition approaches can be grouped into two typical categories: model-based [6–9] and appearance-based [10– 17] .”