Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
Automatic extraction and description of human gait models for recognition purposes
Computer Vision and Image Understanding
Silhouette Analysis-Based Gait Recognition for Human Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Individual Recognition Using Gait Energy Image
IEEE Transactions on Pattern Analysis and Machine Intelligence
Model-Based Hand Tracking Using a Hierarchical Bayesian Filter
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tracking People by Learning Their Appearance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gait recognition using linear time normalization
Pattern Recognition
Knowledge and Information Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Human body gesture recognition using adapted auxiliary particle filtering
AVSS '07 Proceedings of the 2007 IEEE Conference on Advanced Video and Signal Based Surveillance
Canonical Correlation Analysis of Video Volume Tensors for Action Categorization and Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Tensor Decompositions and Applications
SIAM Review
Spatiotemporal salient points for visual recognition of human actions
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Identification of humans using gait
IEEE Transactions on Image Processing
MPCA: Multilinear Principal Component Analysis of Tensor Objects
IEEE Transactions on Neural Networks
A feature sequence kernel for video concept classification
MMM'11 Proceedings of the 17th international conference on Advances in multimedia modeling - Volume Part I
Exploring trace transform for robust human action recognition
Pattern Recognition
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In this paper, we formulate the Relative Margin Support Tensor Machines (RMSTMs) problem as an extension of the Relative Margin Machines (RMMs). While the typical Support Tensor Machines (STMs) find a solution that is greatly influenced by the data spread, the proposed RMSTMs maximize the margin in a way relative to the spread of the data. The difference in the obtained solutions can be significant in the cases of badly scaled data, especially in the case of various spreads across different data dimensions. The efficiency of the proposed method is illustrated on the problems of gait and action recognition, where the results acquired verify the superiority of the method in terms of classification performance.