Learning Patterns of Activity Using Real-Time Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Bayesian Computer Vision System for Modeling Human Interactions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Discovery and Segmentation of Activities in Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiobject Behavior Recognition by Event Driven Selective Attention Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learning variable-length Markov models of behavior
Computer Vision and Image Understanding - Modeling people toward vision-based underatanding of a person's shape, appearance, and movement
International Journal of Computer Vision
Shape Matching and Object Recognition Using Shape Contexts
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Understanding manipulation in video
FG '96 Proceedings of the 2nd International Conference on Automatic Face and Gesture Recognition (FG '96)
Learning Structured Behavior Models Using Variable Length Markov Models
MPEOPLE '99 Proceedings of the IEEE International Workshop on Modelling People
Human Motion: Modeling and Recognition of Actions and Interactions
3DPVT '04 Proceedings of the 3D Data Processing, Visualization, and Transmission, 2nd International Symposium
Body Localization in Still Images Using Hierarchical Models and Hybrid Search
CVPR '06 Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2
Image Processing, Analysis, and Machine Vision
Image Processing, Analysis, and Machine Vision
Solving multiclass learning problems via error-correcting output codes
Journal of Artificial Intelligence Research
CIVR'03 Proceedings of the 2nd international conference on Image and video retrieval
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
A survey on visual surveillance of object motion and behaviors
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Probabilistic posture classification for Human-behavior analysis
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
A semantic event-detection approach and its application to detecting hunts in wildlife video
IEEE Transactions on Circuits and Systems for Video Technology
Recognizing human actions using NWFE-based histogram vectors
EURASIP Journal on Advances in Signal Processing - Special issue on video analysis for human behavior understanding
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This paper presents a new behavior analysis system for analyzing human movements via a boosted string representation. First of all, we propose a triangulation-based method to transform each action sequence into a set of symbols. Then, an action sequence can be interpreted and analyzed using this string representation. To analyze action sequences with this string representation, three practical problems should be tackled. Usually, an action sequence has different temporal scaling changes, different initial states, and symbol converting errors. Traditional methods (like hidden Markov models and finite state machines) have limited abilities to deal with the above problems since many unknown states should be constructed and initialized. To tackle the problems, a novel string hypothesis generator is then proposed for generating a bank of string features from which different invariant features can be learned for classifying behaviors more accurately. To learn the invariant features, the Adaboost algorithm is used and modified to train a strong classifier from the set of string hypotheses so that multiple human action events can be well classified. In addition, a forward classification scheme is proposed to classify all input action sequences more accurately even though they have various scaling changes and coding errors. Experimental results prove that the proposed method is a robust, accurate, and powerful tool for human movement analysis.