Individual recognition from periodic activity using hidden Markov models
HUMO '00 Proceedings of the Workshop on Human Motion (HUMO'00)
Specialized mappings and the estimation of human body pose from a single image
HUMO '00 Proceedings of the Workshop on Human Motion (HUMO'00)
Silhouette-Based Human Identification from Body Shape and Gait
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Gait recognition using image self-similarity
EURASIP Journal on Applied Signal Processing
2D and 3d full-body gesture database for analyzing daily human gestures
ICIC'05 Proceedings of the 2005 international conference on Advances in Intelligent Computing - Volume Part I
Identification of humans using gait
IEEE Transactions on Image Processing
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In this paper, we propose a novel method to reconstruct 3D human body pose for gait recognition from monocular image sequences based on top-down learning. Human body pose is represented by a linear combination of prototypes of 2D silhouette images and their corresponding 3D body models in terms of the position of a predetermined set of joints. With a 2D silhouette image, we can estimate optimal coefficients for a linear combination of prototypes of the 2D silhouette images by solving least square minimization. The 3D body model of the input silhouette image is obtained by applying the estimated coefficients to the corresponding 3D body model of prototypes. In the learning stage, the proposed method is hierarchically constructed by classifying the training data into several clusters recursively. Also, in the reconstructing stage, the proposed method hierarchically reconstructs 3D human body pose with a silhouette image. The experimental results show that our method can be efficient and effective to reconstruct 3D human body pose for gait recognition.