A survey of advances in vision-based human motion capture and analysis
Computer Vision and Image Understanding - Special issue on modeling people: Vision-based understanding of a person's shape, appearance, movement, and behaviour
Computer Vision and Image Understanding
Activity representation using 3D shape models
Journal on Image and Video Processing - Anthropocentric Video Analysis: Tools and Applications
Video event detection using motion relativity and visual relatedness
MM '08 Proceedings of the 16th ACM international conference on Multimedia
Human Activity Recognition with Metric Learning
ECCV '08 Proceedings of the 10th European Conference on Computer Vision: Part I
Unsupervised view and rate invariant clustering of video sequences
Computer Vision and Image Understanding
Human action recognition by feature-reduced Gaussian process classification
Pattern Recognition Letters
Proceedings of the 2nd International Conference on PErvasive Technologies Related to Assistive Environments
Viewpoint manifolds for action recognition
Journal on Image and Video Processing - Special issue on video-based modeling, analysis, and recognition of human motion
Rate-invariant recognition of humans and their activities
IEEE Transactions on Image Processing
Statistical Methods and Models for Video-Based Tracking, Modeling, and Recognition
Foundations and Trends in Signal Processing
Discriminative human action recognition in the learned hierarchical manifold space
Image and Vision Computing
A survey on vision-based human action recognition
Image and Vision Computing
Volumetric Features for Video Event Detection
International Journal of Computer Vision
Characteristic-based descriptors for motion sequence recognition
PAKDD'08 Proceedings of the 12th Pacific-Asia conference on Advances in knowledge discovery and data mining
Tracklet descriptors for action modeling and video analysis
ECCV'10 Proceedings of the 11th European conference on Computer vision: Part I
Human activity analysis: A review
ACM Computing Surveys (CSUR)
A survey of vision-based methods for action representation, segmentation and recognition
Computer Vision and Image Understanding
Computer Vision and Image Understanding
Phase registration of a single quasi-periodic signal using self dynamic time warping
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part III
An unsupervised framework for action recognition using actemes
ACCV'10 Proceedings of the 10th Asian conference on Computer vision - Volume Part IV
Sequence classification via large margin hidden Markov models
Data Mining and Knowledge Discovery
Human action recognition using Pose-based discriminant embedding
Image Communication
Dynamic events as mixtures of spatial and temporal features
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Anomalistic sequence detection
International Journal of Intelligent Information and Database Systems
Image and Vision Computing
Reducing the effect of noise on human contour in gait recognition
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
A tree-based approach to integrated action localization, recognition and segmentation
ECCV'10 Proceedings of the 11th European conference on Trends and Topics in Computer Vision - Volume Part I
Towards never-ending learning from time series streams
Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining
Online human gesture recognition from motion data streams
Proceedings of the 21st ACM international conference on Multimedia
Video event description in scene context
Neurocomputing
A unified tree-based framework for joint action localization, recognition and segmentation
Computer Vision and Image Understanding
Unsupervised categorization of human motion sequences
Intelligent Data Analysis
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An activity consists of an actor performing a series of actions in a pre-defined temporal order. An action is an individual atomic unit of an activity. Different instances of the same activity may consist of varying relative speeds at which the various actions are executed, in addition to other intra- and inter- person variabilities. Most existing algorithms for activity recognition are not very robust to intra- and inter-personal changes of the same activity, and are extremely sensitive to warping of the temporal axis due to variations in speed profile. In this paper, we provide a systematic approach to learn the nature of such time warps while simultaneously allowing for the variations in descriptors for actions. For each activity we learn an 'average' sequence that we denote as the nominal activity trajectory. We also learn a function space of time warpings for each activity separately. The model can be used to learn individualspecific warping patterns so that it may also be used for activity based person identification. The proposed model leads us to algorithms for learning a model for each activity, clustering activity sequences and activity recognition that are robust to temporal, intra- and inter-person variations. We provide experimental results using two datasets.