Behavior histograms for action recognition and human detection

Authors:
Christian Thurau
Affiliations:
Czech Technical University, Faculty of Electrical Engineering, Department for Cybernetics, Center for Machine Perception, Prague 2, Czech Republic
Venue:
Proceedings of the 2nd conference on Human motion: understanding, modeling, capture and animation
Year:
2007

Citing 15
Cited 7

Automated Derivation of Primitives for Movement Classification

Autonomous Robots
Distinctive Image Features from Scale-Invariant Keypoints

International Journal of Computer Vision
Integral Histogram: A Fast Way To Extract Histograms in Cartesian Spaces

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Histograms of Oriented Gradients for Human Detection

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Detection and Explanation of Anomalous Activities: Representing Activities as Bags of Event n-Grams

CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 1 - Volume 01
Actions as Space-Time Shapes

ICCV '05 Proceedings of the Tenth IEEE International Conference on Computer Vision - Volume 2
Simultaneous Tracking and Action Recognition using the PCA-HOG Descriptor

CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
Fast Human Detection Using a Cascade of Histograms of Oriented Gradients

CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Behavior classification by eigendecomposition of periodic motions

Pattern Recognition
n-grams of action primitives for recognizing human behavior

CAIP'07 Proceedings of the 12th international conference on Computer analysis of images and patterns
View-invariant modeling and recognition of human actions using grammars

WDV'05/WDV'06/ICCV'05/ECCV'06 Proceedings of the 2005/2006 international conference on Dynamical vision
Finding motion primitives in human body gestures

GW'05 Proceedings of the 6th international conference on Gesture in Human-Computer Interaction and Simulation
Human motion synthesis by motion manifold learning and motion primitive segmentation

AMDO'06 Proceedings of the 4th international conference on Articulated Motion and Deformable Objects
Human detection using oriented histograms of flow and appearance

ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part II
Single-Histogram class models for image segmentation

ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing

Multi-view Player Action Recognition in Soccer Games

MIRAGE '09 Proceedings of the 4th International Conference on Computer Vision/Computer Graphics CollaborationTechniques
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 descriptor combining MHI and PCOG for human motion classification

Proceedings of the ACM International Conference on Image and Video Retrieval
Recognizing human action from a far field of view

WMVC'09 Proceedings of the 2009 international conference on Motion and video computing
Human action segmentation and recognition via motion and shape analysis

Pattern Recognition Letters
Exploring trace transform for robust human action recognition

Pattern Recognition

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents an approach for human detection and simultaneous behavior recognition from images and image sequences. An action representation is derived by applying a clustering algorithm to sequences of Histogram of Oriented Gradient (HOG) descriptors of human motion images. For novel image sequences, we first detect the human by matching extracted descriptors with the prototypical action primitives. Given a sequence of assigned action primitives, we can build a histogram from observed motion. Thus, behavior can be classified by means of histogram comparison, interpreting behavior recognition as a problem of statistical sequence analysis. Results on publicly available benchmark-data show a high accuracy for action recognition.