Spatiotemporal salient points for visual recognition of human actions

Authors:
A. Oikonomopoulos;I. Patras;M. Pantic
Affiliations:
Comput. Dept., Imperial Coll., London, UK;-;-
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Year:
2005

Citing 0
Cited 39

Unsupervised Learning of Human Action Categories Using Spatial-Temporal Words

International Journal of Computer Vision
Combination of accumulated motion and color segmentation for human activity analysis

Journal on Image and Video Processing - Anthropocentric Video Analysis: Tools and Applications
View-invariant action recognition using interest points

MIR '08 Proceedings of the 1st ACM international conference on Multimedia information retrieval
Handling uncertainty in multimodal pervasive computing applications

Computer Communications
Sparse B-spline polynomial descriptors for human activity recognition

Image and Vision Computing
Social signal processing: Survey of an emerging domain

Image and Vision Computing
A new compositional technique for hand posture recognition

ICCOMP'09 Proceedings of the WSEAES 13th international conference on Computers
Adaptive Tuboid Shapes for Action Recognition

ISVC '09 Proceedings of the 5th International Symposium on Advances in Visual Computing: Part II
Exploiting multi-level parallelism for low-latency activity recognition in streaming video

MMSys '10 Proceedings of the first annual ACM SIGMM conference on Multimedia systems
Human action recognition using boosted EigenActions

Image and Vision Computing
A survey on vision-based human action recognition

Image and Vision Computing
Relative Margin Support Tensor Machines for gait and action recognition

Proceedings of the ACM International Conference on Image and Video Retrieval
Determining the scale of interest regions in videos

ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Discriminative space-time voting for joint recognition and localization of actions.

Proceedings of the 2nd international workshop on Social signal processing
Actor-independent action search using spatiotemporal vocabulary with appearance hashing

Pattern Recognition
A novel middleware solution to improve ubiquitous healthcare systems aided by affective information

IEEE Transactions on Information Technology in Biomedicine - Special section on affective and pervasive computing for healthcare
Discovering motion patterns for human action recognition

PCM'10 Proceedings of the Advances in multimedia information processing, and 11th Pacific Rim conference on Multimedia: Part II
Trajectory-based representation of human actions

ICMI'06/IJCAI'07 Proceedings of the ICMI 2006 and IJCAI 2007 international conference on Artifical intelligence for human computing
Learning attention based saliency in videos from human eye movements

WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
Event detection and recognition for semantic annotation of video

Multimedia Tools and Applications
Aggregating low-level features for human action recognition

ISVC'10 Proceedings of the 6th international conference on Advances in visual computing - Volume Part I
Selective spatio-temporal interest points

Computer Vision and Image Understanding
Editors Choice Article: Structured learning of local features for human action classification and localization

Image and Vision Computing
Event recognition based on a local space-time interest points and self-organization feature map method

ICIC'11 Proceedings of the 7th international conference on Advanced Intelligent Computing
A review on vision techniques applied to Human Behaviour Analysis for Ambient-Assisted Living

Expert Systems with Applications: An International Journal
A Self-Training Approach for Visual Tracking and Recognition of Complex Human Activity Patterns

International Journal of Computer Vision
Higher rank Support Tensor Machines for visual recognition

Pattern Recognition
An efficient approach for multi-view human action recognition based on bag-of-key-poses

HBU'12 Proceedings of the Third international conference on Human Behavior Understanding
FaSTIP: a new method for detection and description of space-time interest points for human activity classification

Proceedings of the Eighth Indian Conference on Computer Vision, Graphics and Image Processing
Simultaneous Bayesian clustering and feature selection using RJMCMC-based learning of finite generalized Dirichlet mixture models

Signal Processing
Mining spatiotemporal video patterns towards robust action retrieval

Neurocomputing
Human action recognition with salient trajectories

Signal Processing
Application of 3D-wavelet statistics to video analysis

Multimedia Tools and Applications
Exploring trace transform for robust human action recognition

Pattern Recognition
A local descriptor based on Laplacian pyramid coding for action recognition

Pattern Recognition Letters
Silhouette-based human action recognition using sequences of key poses

Pattern Recognition Letters
Active learning for human action retrieval using query pool selection

Neurocomputing
Human action categorization using discriminative local spatio-temporal feature weighting

Intelligent Data Analysis
Robust human action recognition scheme based on high-level feature fusion

Multimedia Tools and Applications

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper addresses the problem of human-action recognition by introducing a sparse representation of image sequences as a collection of spatiotemporal events that are localized at points that are salient both in space and time. The spatiotemporal salient points are detected by measuring the variations in the information content of pixel neighborhoods not only in space but also in time. An appropriate distance metric between two collections of spatiotemporal salient points is introduced, which is based on the chamfer distance and an iterative linear time-warping technique that deals with time expansion or time-compression issues. A classification scheme that is based on relevance vector machines and on the proposed distance measure is proposed. Results on real image sequences from a small database depicting people performing 19 aerobic exercises are presented.