An overview of process query systems

Cybenko, George; Berk, Vincent H.; Crespi, Valentino; Jiang, Guofei; Gray, Robert S.

doi:10.1117/12.568330

Cited by 27 publications

(18 citation statements)

References 21 publications

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“…Weak models only describe the possible transitions and possible associations of transitions to states, without assigning any probabilities to them. Weak models have been used in a variety of process detection problems [8].…”

Section: Introductionmentioning

confidence: 99%

The theory of trackability with applications to sensor networks

Crespi

Cybenko

Jiang

2008

ACM Trans. Sen. Netw.

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In this paper, we formalize the concept of tracking in a sensor network and develop a rigorous theory of trackability that investigates the rate of growth of the number of consistent tracks given a sequence of observations made by the sensor network. The phenomenon being tracked is modelled by a nondeterministic nite automaton and the sensor network is modelled by an observer capable of detecting events related, typically ambiguously, to the states of the underlying automaton.More formally, an input string, Z t , of t + 1 symbols (the sensor network observations) that is presented to a nondeterministic nite automaton, M , (the model) determines a set,, of state sequences (the tracks or hypotheses) that are capable of generating the input string Z t . We study the growth of the size of this set, |H M (Z t )|, as a function of the length of the input string, t + 1. Our main result is that for a given automaton and sensor coverage, the worst-case rate of growth is either polynomial or exponential in t, indicating a kind of phase transition in tracking accuracy.The techniques we use include the Joint Spectral Radius, ρ(Σ), of a nite set, Σ, of (0, 1)-matrices derived from M . Specically, we construct a set of matrices, Σ, corresponding to M with the property that ρ(Σ) ≤ 1 if and only if |HM (Z t )| grows polynomially in t. We also prove that for (0, 1)-matrices, the decision problem ρ(Σ) ≤ 1 is Turing decidable and, therefore, so is the problem of deciding whether worst case state sequence growth for a given automaton is polynomial or exponential. These results have applications in sensor networks, computer network security and autonomic computing as well as various tracking problems of recent interest involving detecting phenomena using noisy observations of hidden states.

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Section: Introductionmentioning

confidence: 99%

The theory of trackability with applications to sensor networks

Crespi

Cybenko

Jiang

2008

ACM Trans. Sen. Netw.

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“…Based on the target termination, target detection, new target arrival, and false alarm models described in Section II-A, we have (4) Since with the same are indistinguishable, i.e., invariant under permutation of target indices, they are exchangeable and we assign a uniform prior on them. Hence, (5) Combining (4) and (5), the prior is…”

Section: B Probability Modelmentioning

confidence: 99%

“…M ULTI-TARGET tracking plays an important role in many areas of engineering such as surveillance [1], computer vision [2], [3], network and computer security [4], and sensor networks [5]. In the standard setup, some indistinguishable targets move continuously in a given region, typically independently according to a known, Markovian process.…”

Section: Introductionmentioning

confidence: 99%

Markov chain Monte Carlo data association for general multiple-target tracking problems

Oh¹,

Russell²,

Sastry³

2004

2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601)

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Abstract-This paper presents Markov chain Monte Carlo data association (MCMCDA) for solving data association problems arising in multi-target tracking in a cluttered environment. When the number of targets is fixed, the single-scan version of MCMCDA approximates joint probabilistic data association (JPDA). Although the exact computation of association probabilities in JPDA is NP-hard, we prove that the single-scan MCMCDA algorithm provides a fully polynomial randomized approximation scheme for JPDA. For general multi-target tracking problems, in which unknown numbers of targets appear and disappear at random times, we present a multi-scan MCMCDA algorithm that approximates the optimal Bayesian filter. We also present extensive simulation studies supporting theoretical results in this paper. Our simulation results also show that MCMCDA outperforms multiple hypothesis tracking (MHT) by a significant margin in terms of accuracy and efficiency under extreme conditions, such as a large number of targets in a dense environment, low detection probabilities, and high false alarm rates.Index Terms-Joint probabilistic data association (JPDA), Markov chain Monte Carlo data association (MCMCDA), multiple hypothesis tracking (MHT).

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“…Multiple-target tracking plays an important role in many areas of engineering such as surveillance [2], computer vision [7], network and computer security [8], and sensor networks [19]. If the targets are moving and emit some kind of sound then both audio and video sensors can be utilized.…”

Section: Introductionmentioning

confidence: 99%

Target tracking in urban environments using audio-video signal processing in heterogeneous wireless sensor networks

Kushwaha

Amundson

et al. 2008

2008 42nd Asilomar Conference on Signals, Systems and Computers

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Abstract-Heterogeneous sensor networks (HSNs) with multiple sensing modalities are gaining popularity in diverse fields. In this paper, we describe an approach for target tracking in urban environments utilizing a wireless HSN of mote class devices equipped with acoustic sensor boards and embedded PCs equipped with web cameras. Our system uses acoustic beamforming and motion detection for audio and video sensors, respectively. We also employ MCMCDA algorithm for data association and tracking. Experimental results from a deployment in an urban environment are used to demonstrate our approach.

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An overview of process query systems

Cited by 27 publications

References 21 publications

The theory of trackability with applications to sensor networks

The theory of trackability with applications to sensor networks

Markov chain Monte Carlo data association for general multiple-target tracking problems

Target tracking in urban environments using audio-video signal processing in heterogeneous wireless sensor networks

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