Exploiting Simple Hierarchies for Unsupervised Human Behavior Analysis Fabian Nater Helmut Grabner Luc Van Gool CVPR2010
Feb 24, 2016
Exploiting Simple Hierarchies for Unsupervised Human Behavior Analysis
Fabian Nater Helmut Grabner Luc Van GoolCVPR2010
Abstract
• A data-driven, hierarchical approach for the analysis of human actions in visual scenes
• Completely unsupervised• The model is suitable for coupled tracking and
abnormality detection on different hierarchical stages
Introduction
• Previous work: detect anomalies as outliers to previously trained models
• Our work: supporting autonomous living of elderly or handicapped people
• Rule-based systems: predefined dangerous cases, lacks general applicability
Introduction
• Two hierarchical representations: human appearances and sequences of appearances(actions, behavioral patterns)
• Map these images to a finite set of symbols describing what is observed
• Characterize in which order the observations occur
• learning the temporal (e.g. within a day or a week) and spatial dependencies
Appearance hierarchy
• Image stream ,arbitrary feature space
• Group similar image descriptors together using k-means to create a finite number of clusters
• Distance measuredefined in the feature space
Appearance hierarchy(H1)
• Eventually, each feature vector is mapped to a symbol
• Remove statistical outliers at every clustering step
• Distribution of distances of all the samples assigned to this cluster
Feature extraction
• Background subtraction• Rescaled to fixed size• Distance measure: chi-squared
Action hierarchy(H2)
• Basic actions to encode a state change• Only frequently occurring symbol changes are
considered• Higher level micro-actions are combination of
lower level micro-actions
• Represent image stream as a series of macri-actions of different lengths
Illustration
Anomalies
• H1 will be used for tracking and the interpretation of the appearance, H2 is used for the interpretation of actions
• To decide which cluster the extracted feature belongs to(high dimension), use data-dependent inlier:
• Threshold: 0.05 classified as outlier if its distance to the considered cluster center is larger than 95% of the data in that cluster
Update procedure
• Not all possible appearances and actions can be learnt off-line
• Include frequent appearances classified as outliers
• New leaf node clusters are established and new symbols defined
Update procedure
• Update micro-actions using the principle of exponential forgetting
• Start with empty database, everything considered abnormal at the beginning
Experiments
• Single person in-door videos• 1. Ourliers• 2. Symbols• 3. Action
length
Experiments
• 1. Frequently occurring scenes and abnormal scenes
• 2. Previously normal scenes• 3. New frequent normal scenes• 4. Anomalies
Conclusion
• Unsupervised analysis of human action scenes.
• Two automatically generated and updated hierarchies learned
• Normality and anomaly classification• Allows for model update