Matching Shape Sequences in Video with Applications in Human Movement Analysis
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning, detection and representation of multi-agent events in videos
Artificial Intelligence
Activity representation using 3D shape models
Journal on Image and Video Processing - Anthropocentric Video Analysis: Tools and Applications
Unsupervised view and rate invariant clustering of video sequences
Computer Vision and Image Understanding
Efficient duration and hierarchical modeling for human activity recognition
Artificial Intelligence
Abnormal human behavioral pattern detection in assisted living environments
Proceedings of the 3rd International Conference on PErvasive Technologies Related to Assistive Environments
Path analysis in multiple-target video sequences
ICIAP'11 Proceedings of the 16th international conference on Image analysis and processing - Volume Part II
Integrated tracking and recognition of human activities in shape space
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
Finding "unexplained" activities in video
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Image and Vision Computing
Fitting smoothing splines to time-indexed, noisy points on nonlinear manifolds
Image and Vision Computing
Detection of suspicious behavior from a sparse set of multiagent interactions
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
International Journal of Computer Vision
On the use of a minimal path approach for target trajectory analysis
Pattern Recognition
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The aim is to model "activity" performed by a group of moving and interacting objects (which can be people, cars, or different rigid components of the human body) and use the models for abnormal activity detection. Previous approaches to modeling group activity include co-occurrence statistics (individual and joint histograms) and dynamic Bayesian networks, neither of which is applicable when the number of interacting objects is large. We treat the objects as point objects (referred to as "landmarks") and propose to model their changing configuration as a moving and deforming "shape" (using Kendall's shape theory for discrete landmarks). A continuous-state hidden Markov model is defined for landmark shape dynamics in an activity. The configuration of landmarks at a given time forms the observation vector, and the corresponding shape and the scaled Euclidean motion parameters form the hidden-state vector. An abnormal activity is then defined as a change in the shape activity model, which could be slow or drastic and whose parameters are unknown. Results are shown on a real abnormal activity-detection problem involving multiple moving objects.