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Human Behavior Classification Using Multiple Views
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Outliers in biometrical data: What's old, What's new
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Incremental Video Event Learning
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Human action recognition using boosted EigenActions
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Understanding transit scenes: a survey on human behavior-recognition algorithms
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Detecting and discriminating behavioural anomalies
Pattern Recognition
Event Model Learning from Complex Videos using ILP
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Ubiquitous social perception abilities for interaction initiation in human-robot interaction
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Video topic modelling with behavioural segmentation
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Anomalous video event detection using spatiotemporal context
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Stream-based active unusual event detection
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Multi-scale and real-time non-parametric approach for anomaly detection and localization
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Event detection and classification in video surveillance sequences
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An entropy approach for abnormal activities detection in video streams
Pattern Recognition
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Public Space Behavior Modeling With Video and Sensor Analytics
Bell Labs Technical Journal
Video Behaviour Mining Using a Dynamic Topic Model
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Detection of similar sequences in EEG maps series using correlation coefficients matrix
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Exploratory search of long surveillance videos
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Learning common behaviors from large sets of unlabeled temporal series
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Visual code-sentences: a new video representation based on image descriptor sequences
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Mobile Networks and Applications
Activity clustering for anomaly detection
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This paper aims to address the problem of modelling video behaviour captured in surveillancevideos for the applications of online normal behaviour recognition and anomaly detection. A novelframework is developed for automatic behaviour profiling and online anomaly sampling/detectionwithout any manual labelling of the training dataset. The framework consists of the followingkey components: (1) A compact and effective behaviour representation method is developed basedon discrete scene event detection. The similarity between behaviour patterns are measured basedon modelling each pattern using a Dynamic Bayesian Network (DBN). (2) Natural grouping ofbehaviour patterns is discovered through a novel spectral clustering algorithm with unsupervisedmodel selection and feature selection on the eigenvectors of a normalised affinity matrix. (3) Acomposite generative behaviour model is constructed which is capable of generalising from asmall training set to accommodate variations in unseen normal behaviour patterns. (4) A run-timeaccumulative anomaly measure is introduced to detect abnormal behaviour while normal behaviourpatterns are recognised when sufficient visual evidence has become available based on an onlineLikelihood Ratio Test (LRT) method. This ensures robust and reliable anomaly detection and normalbehaviour recognition at the shortest possible time. The effectiveness and robustness of our approachis demonstrated through experiments using noisy and sparse datasets collected from both indoorand outdoor surveillance scenarios. In particular, it is shown that a behaviour model trained usingan unlabelled dataset is superior to those trained using the same but labelled dataset in detectinganomaly from an unseen video. The experiments also suggest that our online LRT based behaviourrecognition approach is advantageous over the commonly used Maximum Likelihood (ML) methodin differentiating ambiguities among different behaviour classes observed online.