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Particle Methods for Change Detection, System Identification, and Control
 Proceedings of the IEEE
, 2004
"... this paper is to provide a detailed overview of them ..."
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Cited by 33 (0 self)
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this paper is to provide a detailed overview of them
"Shape Activity": A Continuous State HMM for Moving/Deforming Shapes with Application to Abnormal Activity Detection
"... The aim is to model "activity" performed by a group of moving and interacting objects (which can be people or cars or different rigid components of the human body) and use the models for abnormal activity detection. Previous approaches to modeling group activity include cooccurrence statistics (ind ..."
Abstract

Cited by 19 (10 self)
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The aim is to model "activity" performed by a group of moving and interacting objects (which can be people or cars or different rigid components of the human body) and use the models for abnormal activity detection. Previous approaches to modeling group activity include cooccurrence 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 (HMM) 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.
Change Detection in Partially Observed Nonlinear Dynamic Systems with Unknown Change Parameters
 in American Control Conference (ACC
, 2004
"... We study the change detection problem in partially observed nonlinear dynamic systems. We assume that the change parameters are unknown and the change could be gradual (slow) or sudden (drastic). For most nonlinear systems, no finite dimensional filters exist and approximation filtering methods like ..."
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Cited by 16 (14 self)
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We study the change detection problem in partially observed nonlinear dynamic systems. We assume that the change parameters are unknown and the change could be gradual (slow) or sudden (drastic). For most nonlinear systems, no finite dimensional filters exist and approximation filtering methods like the Particle Filter are used. Even when change parameters are unknown, drastic changes can be detected easily using the increase in tracking (output) error or the negative log of observation likelihood (OL). But slow changes usually get missed. We propose in this paper, a statistic for slow change detection which turns out to be the same as the Kerridge Inaccuracy between the posterior state distribution and the normal system prior. We show asymptotic convergence (under certain assumptions) of the bounding, modeling and particle filtering errors in its approximation using a particle filter optimal for the normal system. We also demonstrate using the bounds on the errors that our statistic works in situations where observation likelihood (OL) fails and vice versa.
Shape Activity”: a continuousstate HMM for moving/deforming shapes with application to abnormal activity detection
 IEEE Transactions on Image Processing
, 2005
"... Abstract—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 cooccurrence statis ..."
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Cited by 13 (2 self)
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Abstract—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 cooccurrence 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 continuousstate 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 hiddenstate 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 activitydetection problem involving multiple moving objects. Index Terms—Abnormal acitivity detection, activity recognition, hidden Markov model (HMM), landmark shape dynamics, particle filtering, shape activity. I.
Additive change detection in nonlinear systems with unknown change parameters
 IEEE Trans. Sig. Proc
, 2007
"... Abstract—We study the change detection problem in partially observed, nonlinear systems [which satisfy the hidden Markov model (HMM) property]. The change parameters are assumed unknown, and the changes can be slow or sudden. A partially observed system needs to be tracked first before changes can b ..."
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Cited by 11 (4 self)
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Abstract—We study the change detection problem in partially observed, nonlinear systems [which satisfy the hidden Markov model (HMM) property]. The change parameters are assumed unknown, and the changes can be slow or sudden. A partially observed system needs to be tracked first before changes can be detected. Sudden changes result in significant loss of track. These can be detected easily using the increase in tracking error (TE) or observation likelihood (OL) or using a CUSUMtype method applied to either of these. However, slow changes (which result in small loss of track) often get missed. We propose here a statistic that uses the tracked component of the change to detect it and, hence, detects slow changes faster than TE or OL. We show, both analytically and through simulations, that this statistic complements OL and TE for change detection. Index Terms—Abnormality detection, change detection, particle filtering, tracking, unknown change parameters. I.
THE MODIFIED CUSUM ALGORITHM FOR SLOW AND DRASTIC CHANGE DETECTION IN GENERAL HMMS WITH UNKNOWN CHANGE PARAMETERS
"... We study the change detection problem in a general HMM when the change parameters are unknown and the change can be slow or drastic. Drastic changes can be detected easily using the increase in tracking error or the negative log of observation likelihood (OL). But slow changes usually get missed. We ..."
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Cited by 2 (1 self)
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We study the change detection problem in a general HMM when the change parameters are unknown and the change can be slow or drastic. Drastic changes can be detected easily using the increase in tracking error or the negative log of observation likelihood (OL). But slow changes usually get missed. We have proposed in past work a statistic called ELL which works for slow change detection. Now single time estimates of any statistic can be noisy. Hence we propose a modification of the Cumulative Sum (CUSUM) algorithm which can be applied to ELL and OL and thus improves both slow and drastic change detection performance. 1.
IEEE TRANSACTIONS ON IMAGE PROCESSING 1 “Shape Activity”: A Continuous State HMM for Moving/Deforming Shapes with Application to Abnormal Activity Detection
"... Abstract — The aim is to model “activity ” performed by a group of moving and interacting objects (which can be people or cars or different rigid components of the human body) and use the models for abnormal activity detection. Previous approaches to modeling group activity include cooccurrence sta ..."
Abstract
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Abstract — The aim is to model “activity ” performed by a group of moving and interacting objects (which can be people or cars or different rigid components of the human body) and use the models for abnormal activity detection. Previous approaches to modeling group activity include cooccurrence 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 (HMM) 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. I.
1 “Shape Activity”: A Continuous State HMM for Moving/Deforming Shapes with Application to Abnormal Activity Detection
"... Abstract — The aim is to model “activity ” performed by a group of moving and interacting objects (which can be people or cars or different rigid components of the human body) and use the models for abnormal activity detection. We treat the objects as point objects (referred to as ‘landmarks ’ in sh ..."
Abstract
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Abstract — The aim is to model “activity ” performed by a group of moving and interacting objects (which can be people or cars or different rigid components of the human body) and use the models for abnormal activity detection. We treat the objects as point objects (referred to as ‘landmarks ’ in shape theory literature) and model their changing configuration as a moving and deforming “shape ” using ideas from Kendall’s shape theory for discrete landmarks. A continuous state HMM (Hidden Markov Model) which takes the objects ’ configuration as the observation and the shape and motion as the hidden state, is defined to represent an activity and called a “shape activity”. Particle filters are used to track the HMM i.e. estimate the hidden state (shape, motion) given observations. An abnormal activity is then defined as a change in the shape activity model with the change parameters being unknown and the change can be slow or drastic. Results are shown on a real abnormal activity detection problem involving multiple moving objects. I.
1 Slow and Drastic Change Detection in General HMMs Using Particle Filters with Unknown Change Parameters
"... We study the change detection problem in general HMMs, when change parameters are unknown and the change could be gradual (slow) or sudden (drastic). Drastic changes can be detected easily using the increase in tracking error or the negative log of the observation likelihood conditioned on past obse ..."
Abstract
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We study the change detection problem in general HMMs, when change parameters are unknown and the change could be gradual (slow) or sudden (drastic). Drastic changes can be detected easily using the increase in tracking error or the negative log of the observation likelihood conditioned on past observations (OL). But slow changes usually get missed. We propose a statistic for slow change detection called ELL which is the conditional Expectation of the negative Log Likelihood of the state given past observations. We show asymptotic stability (stability under weaker assumptions) of the errors in approximating the ELL for changed observations using a particle filter that is optimal for the unchanged system. It is shown that the upper bound on ELL error is an increasing function of the “rate of change ” with increasing derivatives of all orders, and its implications are discussed. We also demonstrate, using the bounds on the errors, the complementary behavior of ELL and OL. Results are shown for simulated examples and for a real abnormal activity detection problem. I.