A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
The robust estimation of multiple motions: parametric and piecewise-smooth flow fields
Computer Vision and Image Understanding
Pfinder: Real-Time Tracking of the Human Body
IEEE Transactions on Pattern Analysis and Machine Intelligence
CONDENSATION—Conditional Density Propagation forVisual Tracking
International Journal of Computer Vision
The visual analysis of human movement: a survey
Computer Vision and Image Understanding
Fast Lighting Independent Background Subtraction
International Journal of Computer Vision - Special issue on a special section on visual surveillance
Robust Real-Time Periodic Motion Detection, Analysis, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovery and Segmentation of Activities in Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning and Classification of Complex Dynamics
IEEE Transactions on Pattern Analysis and Machine Intelligence
The Recognition of Human Movement Using Temporal Templates
IEEE Transactions on Pattern Analysis and Machine Intelligence
A survey of computer vision-based human motion capture
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
Color-Based Probabilistic Tracking
ECCV '02 Proceedings of the 7th European Conference on Computer Vision-Part I
Coupled hidden Markov models for complex action recognition
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Invariant features for 3-D gesture recognition
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
A framework for motion recognition with applications to American sign language and gait recognition
HUMO '00 Proceedings of the Workshop on Human Motion (HUMO'00)
Human Motion Signatures: Analysis, Synthesis, Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 3 - Volume 3
A Mixed-State Condensation Tracker with Automatic Model-Switching
ICCV '98 Proceedings of the Sixth International Conference on Computer Vision
A real-time head nod and shake detector
Proceedings of the 2001 workshop on Perceptive user interfaces
The HumanID Gait Challenge Problem: Data Sets, Performance, and Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
CRV '05 Proceedings of the 2nd Canadian conference on Computer and Robot Vision
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
Summarising contextual activity and detecting unusual inactivity in a supportive home environment
Pattern Analysis & Applications
Automated Detection of Unusual Events on Stairs
CRV '06 Proceedings of the The 3rd Canadian Conference on Computer and Robot Vision
Learning and inferring transportation routines
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
A Rao-Blackwellized particle filter for EigenTracking
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Multi-granularity video unusual event detection based on infinite Hidden Markov models
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
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This paper presents a method for automatically detecting unusual human events on stairs from video data. The motivation is to provide a tool for biomedical researchers to rapidly find the events of interest within large quantities of video data. Our system identifies potential sequences containing anomalies, and reduces the amount of data that needs to be searched by a human. We compute two sets of features from a video of a person descending a stairwell. The first set of features are the foot positions and velocities. We track both feet using a mixed state particle filter with an appearance model based on histograms of oriented gradients. We compute expected (most likely) foot positions given the state of the filter at each frame. The second set of features are the parameters of the mean optical flow over a foreground region. Our final classification system inputs these two sets of features into a hidden Markov model (HMM) to analyse the spatio-temporal progression of the stair descent. A single HMM is trained on sequences of normal stair use, and a threshold on sequence likelihoods is used to detect unusual events in new data. We demonstrate our system on a data set with five people descending a set of stairs in a laboratory environment. We show how our system can successfully detect nearly all anomalous events, with a low false positive rate. We discuss limitations and suggest improvements to the system.