Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Moving object recognition in eigenspace representation: gait analysis and lip reading
Pattern Recognition Letters
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discriminant Analysis for Recognition of Human Face Images (Invited Paper)
AVBPA '97 Proceedings of the First International Conference on Audio- and Video-Based Biometric Person Authentication
The Representation and Recognition of Human Movement Using Temporal Templates
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
An Appearance-Based Representation of Action
ICPR '96 Proceedings of the 1996 International Conference on Pattern Recognition (ICPR '96) Volume I - Volume 7270
Foreground object detection from videos containing complex background
MULTIMEDIA '03 Proceedings of the eleventh ACM international conference on Multimedia
Articulated Body Motion Capture by Stochastic Search
International Journal of Computer Vision
Computational studies of human motion: part 1, tracking and motion synthesis
Foundations and Trends® in Computer Graphics and Vision
Shadow detection for moving objects based on texture analysis
Pattern Recognition
Aging in place: fall detection and localization in a distributed smart camera network
Proceedings of the 15th international conference on Multimedia
Searching for Complex Human Activities with No Visual Examples
International Journal of Computer Vision
A vision-based analysis system for gait recognition in patients with Parkinson's disease
Expert Systems with Applications: An International Journal
Temporal motion recognition and segmentation approach
International Journal of Imaging Systems and Technology - Contemporary Challenges in Combinatorial Image Analysis
Variable silhouette energy image representations for recognizing human actions
Image and Vision Computing
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
Two-frame motion estimation based on polynomial expansion
SCIA'03 Proceedings of the 13th Scandinavian conference on Image analysis
A fall detection system using k-nearest neighbor classifier
Expert Systems with Applications: An International Journal
Dynamic textures for human movement recognition
Proceedings of the ACM International Conference on Image and Video Retrieval
Object Detection with Discriminatively Trained Part-Based Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sensor-driven agenda for intelligent home care of the elderly
Expert Systems with Applications: An International Journal
Elderly activities recognition and classification for applications in assisted living
Expert Systems with Applications: An International Journal
Real-time fall detection and activity recognition using low-cost wearable sensors
ICCSA'13 Proceedings of the 13th international conference on Computational Science and Its Applications - Volume 1
Fall detection for multiple pedestrians using depth image processing technique
Computer Methods and Programs in Biomedicine
A wearable sensor based approach to real-time fall detection and fine-grained activity recognition
Journal of Mobile Multimedia
Hi-index | 12.05 |
Automatic recognition of anomalous human activities and falls in an indoor setting from video sequences could be an enabling technology for low-cost, home-based health care systems. Detection systems based upon intelligent computer vision software can greatly reduce the costs and inconveniences associated with sensor based systems. In this paper, we propose such a software based upon a spatio-temporal motion representation, called Motion Vector Flow Instance (MVFI) templates, that capture relevant velocity information by extracting the dense optical flow from video sequences of human actions. Automatic recognition is achieved by first projecting each human action video sequence, consisting of approximately 100 images, into a canonical eigenspace, and then performing supervised learning to train multiple actions from a large video database. We show that our representation together with a canonical transformation with PCA and LDA of image sequences provides excellent action discrimination. We also demonstrate that by including both the magnitude and direction of the velocity into the MVFI, sequences with abrupt velocities, such as falls, can be distinguished from other daily human action with both high accuracy and computational efficiency. As an added benefit, we demonstrate that, once trained, our method for detecting falls is robust and we can attain real-time performance.