Multiple target detection and track identification using modified high order correlations

Liou, Ren-Jean; Azimi-Sadjadi, M.R.

doi:10.1109/icnn.1994.374761

Cited by 11 publications

(13 citation statements)

References 7 publications

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“…The real-time implementation of these methods using connectionist networks was also presented [171] which showed the potential of these schemes for parallel implementation.…”

Section: ) 3-d Matched Filtersmentioning

confidence: 99%

“…In [171], a track scoring mechanism is proposed and a new method is developed to perform data association and track identification in the presence of heavy clutter using the modified HOC. This method was modified by imposing velocity and curvature constraints in order to reject false tracks even at a greater degree and improve clutter rejection performance.…”

Section: ) 3-d Matched Filtersmentioning

confidence: 99%

See 1 more Smart Citation

A Survey of Motion-Based Multitarget Tracking Methods

Qiu¹,

Zhang²,

Lü³

et al. 2015

PIER B

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Abstract-Multitarget tracking (MTT) in surveillance system is extremely challenging, due to uncertain data association, maneuverable target motion, dense clutter disturbance, and real-time processing requirements. A good many methods have been proposed to cope with these challenges. However, no up-to-date survey is available in the literature that can help to select suitable tracking algorithm for practical problem. This paper provides a comprehensive review of the state-of-the-art motion-based MTT techniques, classifies existing methods into two groups, i.e., the detect-beforetrack (DBT) scheme and the track-before-detect (TBD) scheme. The DBT scheme is employed to achieve robust and tractable tracking performance when the signal-noise-ratio (SNR) is strong. The TBD scheme is used in the scenarios of low SNR, and it aims to cumulate target energy by multiple sensor frames. Furthermore, depending on the data association mechanism, the DBT methods can be classified into two categories, data association based approaches and finite set statistics (FISST) based approaches. And the TBD methods can be classified into non-Bayesian approaches and Bayesian approaches depending on the basis theory used for tracking. For each category, this paper provides the detailed descriptions of the representative algorithms, and examines their pros and cons. Finally, new trends for future research directions are offered.

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“…The real-time implementation of these methods using connectionist networks was also presented [171] which showed the potential of these schemes for parallel implementation.…”

Section: ) 3-d Matched Filtersmentioning

confidence: 99%

Section: ) 3-d Matched Filtersmentioning

confidence: 99%

A Survey of Motion-Based Multitarget Tracking Methods

Qiu¹,

Zhang²,

Lü³

et al. 2015

PIER B

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show abstract

“…where v is the maximum allowable target movement from one scan to the next and yo represents how To determine the spatial-temporal correlation of more than two data points, and also impose both velocity and curvature limitations for the moving target, we can get a three consecutive scan RHOC equation [2] as…”

Section: Target Detection and Clutter Rejectionmentioning

confidence: 99%

Neural network directed Bayes decision rule for moving target classification

Azimi-Sadjadi²

2000

IEEE Trans. Aerosp. Electron. Syst.

Self Cite

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In this paper, a new neural network directed Bayes decision rule is developed for target classification exploiting the dynamic behavior of the target. The system consists of a feature extractor, a neural network directed conditional probability generator and a novel sequential Bayes classifier. The velocity and curvature sequences extracted from each track are used as the primary features. Similar to hidden Markov model (HMM) scheme, several hidden states are used to train the neural network, the output of which is the conditional probability of occurring the hidden states given the observations. These conditional probabilities are then used as the inputs to the sequential Bayes classifier to make the classification. The classification results are updated recursively whenever a new scan of data is received. Simulation results on multiscan images containing heavy clutter are presented to demonstrate the effectiveness of the proposed methods.

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“…As the key to sequence trajectory analysis is based on the motion continuity of the target and the randomness of noises to eliminate the influence of false targets, adverse to the detection of dim and small moving targets submerged in various noises and clutters, the DBT algorithm is only applied to scenes with high signal-tonoise ratio (SNR > 5 dB). In order to detect the targets in the case of low SNR, researchers also propose TBD algorithm [1][2][3][4][5]. The TBD algorithm firstly searches all possible trajectories of the target and applies the appropriate method to complete the interframe energy cumulates.…”

Section: Introductionmentioning

confidence: 99%

“…Various methods proposed in literature [1][2][3][4][5] need to know a priori knowledge of the target, including the movement state and trajectory. In the actual infrared scene, scope of application of those methods is undoubtedly.…”

Section: Introductionmentioning

confidence: 99%

Infrared Dim and Small Targets Detection Method Based on Local Energy Center of Sequential Image

Fan

Zhang

et al. 2017

Mathematical Problems in Engineering

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In order to detect infrared (IR) dim and small targets in a strong clutter background, a method based on local energy center of sequential image is proposed. This paper began by using improved anisotropy for background prediction (IABP), followed by target enhancement by improved high-order cumulates (HOC). Finally, on the basis of image preprocessing, the paper constructs a sequential image energy center detection algorithm that integrates the neighborhood, continuity, area, and energy and other motion characteristics of the target. Experiments showed that the improved anisotropic background predication could be loyal to the true background of the original image to the maximum extent, presenting a superior overall performance to other background prediction methods; the improved HOC significantly increased the signal-noise ratio of images; when the signal-noise ratio (SNR) is lower than 2.5 dB, the proposed method could effectively eliminate noise and detect targets.

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Multiple target detection and track identification using modified high order correlations

Cited by 11 publications

References 7 publications

A Survey of Motion-Based Multitarget Tracking Methods

A Survey of Motion-Based Multitarget Tracking Methods

Neural network directed Bayes decision rule for moving target classification

Infrared Dim and Small Targets Detection Method Based on Local Energy Center of Sequential Image

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