Motion texture: a two-level statistical model for character motion synthesis
Proceedings of the 29th annual conference on Computer graphics and interactive techniques
International Journal of Computer Vision
Learning and Recognizing Human Dynamics in Video Sequences
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Bernoulli Mixture Models for Binary Images
ICPR '04 Proceedings of the Pattern Recognition, 17th International Conference on (ICPR'04) Volume 3 - Volume 03
Matching Shape Sequences in Video with Applications in Human Movement Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Individual Recognition Using Gait Energy Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Improved Gait Recognition by Gait Dynamics Normalization
IEEE Transactions on Pattern Analysis and Machine Intelligence
Transformation invariant component analysis for binary images
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 1
IEEE Transactions on Pattern Analysis and Machine Intelligence
Modeling, Clustering, and Segmenting Video with Mixtures of Dynamic Textures
IEEE Transactions on Pattern Analysis and Machine Intelligence
Frame difference energy image for gait recognition with incomplete silhouettes
Pattern Recognition Letters
Hybrid Dynamical Models of Human Motion for the Recognition of Human Gaits
International Journal of Computer Vision
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fusion of static and dynamic body biometrics for gait recognition
IEEE Transactions on Circuits and Systems for Video Technology
Human action recognition employing negative space features
Journal of Visual Communication and Image Representation
Video content categorization using the double decomposition
Multimedia Tools and Applications
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In this paper, we proposed an improved two-level dynamic Bayesian network layered time series model (LTSM), which aims to solve the limitations hindering the application of available dynamic Bayesian networks, the hidden Markov model (HMM) and the dynamic texture (DT) model to gait recognition. In the first level, a gait silhouette or feature cycle is divided into several temporally adjacent clusters. Each cluster is modeled by a DT or logistic DT (LDT). In the second level, HMM is built to describe the relationship among the DTs/LDTs. Besides LTSM, LDT is also an improved dynamic Bayesian network presented in this paper to describe the binary image sequence, which introduces the logistic principle component analysis (PCA) to learning its parameters. We demonstrated the validity of LTSM with experiments on both the CMU Mobo gait database and CASIA gait database (dataset B), and that of LDT on the CMU Mobo gait database. Experimental results showed the superiority of the improved dynamic Bayesian networks.