Results 1  10
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59
Machine recognition of human activities: A survey
, 2008
"... The past decade has witnessed a rapid proliferation of video cameras in all walks of life and has resulted in a tremendous explosion of video content. Several applications such as contentbased video annotation and retrieval, highlight extraction and video summarization require recognition of the a ..."
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Cited by 218 (0 self)
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The past decade has witnessed a rapid proliferation of video cameras in all walks of life and has resulted in a tremendous explosion of video content. Several applications such as contentbased video annotation and retrieval, highlight extraction and video summarization require recognition of the activities occurring in the video. The analysis of human activities in videos is an area with increasingly important consequences from security and surveillance to entertainment and personal archiving. Several challenges at various levels of processing—robustness against errors in lowlevel processing, view and rateinvariant representations at midlevel processing and semantic representation of human activities at higher level processing—make this problem hard to solve. In this review paper, we present a comprehensive survey of efforts in the past couple of decades to address the problems of representation, recognition, and learning of human activities from video and related applications. We discuss the problem at two major levels of complexity: 1) “actions ” and 2) “activities. ” “Actions ” are characterized by simple motion patterns typically executed by a single human. “Activities ” are more complex and involve coordinated actions among a small number of humans. We will discuss several approaches and classify them according to their ability to handle varying degrees of complexity as interpreted above. We begin with a discussion of approaches to model the simplest of action classes known as atomic or primitive actions that do not require sophisticated dynamical modeling. Then, methods to model actions with more complex dynamics are discussed. The discussion then leads naturally to methods for higher level representation of complex activities.
An Algebraic Geometric Approach to the Identification of a Class of Linear Hybrid Systems
 In Proc. of IEEE Conference on Decision and Control
, 2003
"... We propose an algebraic geometric solution to the identification of a class of linear hybrid systems. We show that the identification of the model parameters can be decoupled from the inference of the hybrid state and the switching mechanism generating the transitions, hence we do not constraint the ..."
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Cited by 60 (15 self)
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We propose an algebraic geometric solution to the identification of a class of linear hybrid systems. We show that the identification of the model parameters can be decoupled from the inference of the hybrid state and the switching mechanism generating the transitions, hence we do not constraint the switches to be separated by a minimum dwell time. The decoupling is obtained from the socalled hybrid decoupling constraint, which establishes a connection between linear hybrid system identification, polynomial factorization and hyperplane clustering. In essence, we represent the number of discrete states n as the degree of a homogeneous polynomial p and the model parameters as factors of p. We then show that one can estimate n from a rank constraint on the data, the coe#cients of p from a linear system, and the model parameters from the derivatives of p. The solution is closed form if and only if n 4. Once the model parameters have been identified, the estimation of the hybrid state becomes a simpler problem. Although our algorithm is designed for noiseless data, we also present simulation results with noisy data. 1
Observability of linear hybrid systems, in:
 Lecture Notes in Computer Science,
, 2003
"... Abstract. We analyze the observability of the continuous and discrete states of continuoustime linear hybrid systems. For the class of jumplinear systems, we derive necessary and sufficient conditions that the structural parameters of the model must satisfy in order for filtering and smoothing alg ..."
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Cited by 44 (5 self)
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Abstract. We analyze the observability of the continuous and discrete states of continuoustime linear hybrid systems. For the class of jumplinear systems, we derive necessary and sufficient conditions that the structural parameters of the model must satisfy in order for filtering and smoothing algorithms to operate correctly. Our conditions are simple rank tests that exploit the geometry of the observability subspaces. For linear hybrid systems, we derive weaker rank conditions that are sufficient to guarantee the uniqueness of the reconstruction of the state trajectory, even when the individual linear systems are unobservable.
Observability of switched linear systems in continuous time
 of Lecture Notes in Computer Sciences
, 2005
"... Abstract. We study continuoustime switched linear systems with unobserved and exogenous mode signals. We analyze the observability of the initial state and initial mode under arbitrary switching, and characterize both properties in both the autonomous and nonautonomous cases. 1 ..."
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Cited by 28 (1 self)
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Abstract. We study continuoustime switched linear systems with unobserved and exogenous mode signals. We analyze the observability of the initial state and initial mode under arbitrary switching, and characterize both properties in both the autonomous and nonautonomous cases. 1
A sparsification approach to set membership identification of a class of affine hybrid systems
 IEEE Transactions on Automatic Control
"... A sparsification approach to set membership identification of a class of affine hybrid systems ..."
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Cited by 27 (12 self)
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A sparsification approach to set membership identification of a class of affine hybrid systems
Bayesian nonparametric inference of switching linear dynamical systems
, 2010
"... Abstract—Many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switching linear dynamical system (SLDS) and the switching vector autoregressive (VAR) process. Our Bayesian nonparamet ..."
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Cited by 25 (4 self)
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Abstract—Many complex dynamical phenomena can be effectively modeled by a system that switches among a set of conditionally linear dynamical modes. We consider two such models: the switching linear dynamical system (SLDS) and the switching vector autoregressive (VAR) process. Our Bayesian nonparametric approach utilizes a hierarchical Dirichlet process prior to learn an unknown number of persistent, smooth dynamical modes. We additionally employ automatic relevance determination to infer a sparse set of dynamic dependencies allowing us to learn SLDS with varying state dimension or switching VAR processes with varying autoregressive order. We develop a sampling algorithm that combines a truncated approximation to the Dirichlet process with efficient joint sampling of the mode and state sequences. The utility and flexibility of our model are demonstrated on synthetic data, sequences of dancing honey bees, the IBOVESPA stock index and a maneuvering target tracking application. Index Terms—Autoregressive processes, Bayesian methods, hidden Markov models, statespace methods, time series analysis,
Observability of switched linear systems
 In Hybrid Systems: Computation and Control, LNCS
, 2004
"... Abstract. The observability of deterministic, discretetime, switched, linear systems is considered. Depending on whether or not the modes are observed, and on whether the continuous state or the mode sequence is to be recovered, several observability concepts are defined, characterized through line ..."
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Cited by 21 (1 self)
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Abstract. The observability of deterministic, discretetime, switched, linear systems is considered. Depending on whether or not the modes are observed, and on whether the continuous state or the mode sequence is to be recovered, several observability concepts are defined, characterized through linear algebraic tests, and their decidability assessed. 1
Observability criteria and estimator design for stochastic linear hybrid systems
 in Proceedings of the IEE European Control Conference
, 2003
"... systems A stochastic linear hybrid system is said to be observable if the hybrid state of the system can be uniquely determined from its output. In this paper, we derive conditions for the observability of stochastic linear hybrid systems by exploiting the information obtained from system noise char ..."
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Cited by 14 (2 self)
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systems A stochastic linear hybrid system is said to be observable if the hybrid state of the system can be uniquely determined from its output. In this paper, we derive conditions for the observability of stochastic linear hybrid systems by exploiting the information obtained from system noise characteristics. Having established the necessary criteria for observability, we study the effect of these conditions on estimator design, and also find bounds on the switching times of the system to achieve guaranteed estimator performance. We then apply these results to the estimation of a twomode aircraft trajectory. 1
Flightmodebased aircraft conflict detection using a residualmean interacting multiple model algorithm
 in: Proceedings of the AIAA Guidance, Navigation, and Control Conference
, 2003
"... Based on the trajectory prediction error model proposed by Paielli and Erzberger, we propose nominal and probabilistic conflict detection algorithms using flight mode estimates as well as the aircraft current state estimates. This is different from previous conflict detection algorithms which use cu ..."
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Cited by 13 (2 self)
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Based on the trajectory prediction error model proposed by Paielli and Erzberger, we propose nominal and probabilistic conflict detection algorithms using flight mode estimates as well as the aircraft current state estimates. This is different from previous conflict detection algorithms which use current state estimates only. Our algorithms are therefore based on hybrid models of aircraft, which allow for both continuous dynamics and discrete mode switching. To obtain accurate state and mode estimates, we propose a modified version of the Interacting Multiple Model (IMM) algorithm designed by BarShalom et al. called the ResidualMean Interacting Multiple Model (RMIMM) method. RMIMM is a multiplemodelbased estimation algorithm based on a new likelihood function which uses the mean of the residual produced by each mode matched filter (usually Kalman filter), producing better mode estimates, and therefore better state estimates, than in the IMM case. We demonstrate our algorithm on multiple aircraft scenarios, and in the latter part of the paper, the probabilistic conflict detection algorithm is combined with the protocolbased conflict resolution algorithm, designed by the authors in earlier work.
Subspace identification of piecewise linear systems
 In Proceedings of the 43rd IEEE Conference on Decision and Control
, 2005
"... AbstractSubspace identification can be used to obtain models of piecewise linear statespace systems for which the switching is known. The models should not switch faster than the block size of the Hankel matrices used. The nonconsecutive parts of the input and output data that correspond to one o ..."
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Cited by 12 (0 self)
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AbstractSubspace identification can be used to obtain models of piecewise linear statespace systems for which the switching is known. The models should not switch faster than the block size of the Hankel matrices used. The nonconsecutive parts of the input and output data that correspond to one of the local linear systems can be used to obtain the system matrices of that system up to a linear state transformation. The linear systems obtained in this way cannot be combined directly, because the state transformation is different for each of the local linear systems. The transitions between the local linear systems can be used to transform the models to the same state space basis. We show that the necessary transformations can be obtained from the data, if the data contains a sufficiently large number of transitions for which the states at the transition are linearly independent. An algorithm to determine the transformations is presented, and the sensitivity with respect to noise is investigated using a MonteCarlo simulation.