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Cited by 22 publications
(14 citation statements)
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“…The infinite HMM modelling retains the full posterior density function as well as the underlying HMM states. Zhang et al [35] propose an abnormal event detection algorithm from video sequences using a three-phased approach. Firstly, they build a set of weak classifiers using Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) and then use ensemble learning to identify abnormal events.…”
Section: Related Workmentioning
confidence: 99%
“…The infinite HMM modelling retains the full posterior density function as well as the underlying HMM states. Zhang et al [35] propose an abnormal event detection algorithm from video sequences using a three-phased approach. Firstly, they build a set of weak classifiers using Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) and then use ensemble learning to identify abnormal events.…”
Section: Related Workmentioning
confidence: 99%
“…However, most of it is based on cameras [6,7], wearable sensors [8,9] and RFID sensors [10,11], and studies on AAR in AmI environments using non-obtrusive and pervasive sensors are rare. In 2007 and 2008, Jakkula et al [12,13] proposed recognizing abnormal activities using non-obtrusive sensors.…”
Section: Open Accessmentioning
confidence: 99%
“…Visual words are clustered into actions, and clips are clustered into behaviors over co-occurring actions. Both [9] and [10] use a Hierarchical Dirichlet Process Hidden Markov Model (HDP-HMM) for state detection. In this model, HDP can automatically decide the number of states for HMM.…”
Section: Fig 1 Single-agent Motion Patterns and Interaction Patternsmentioning
confidence: 99%