A recursive moving-target-indication algorithm for optical image sequences

Reed, I. S.; Gagliardi, R.; Stotts, Larry B.

doi:10.1109/7.106120

Cited by 120 publications

(47 citation statements)

References 14 publications

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“…The well-known approach is TrackBefore-Detect (TBD), which is the same concept as 3D matched filter. The TBD method can remove static clutter such as ground clutter [10,11] and detect targets in a sun-glint environment [12]. Dynamic programming (DP), which is a fast version of the traditional TBD, achieves powerful performance in detecting dim targets [13,14].…”

Section: Target Information-based Target Detectionmentioning

confidence: 99%

Double Layered-Background Removal Filter for Detecting Small Infrared Targets in Heterogenous Backgrounds

Kim

2010

J Infrared Milli Terahz Waves

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Detecting small targets is essential for mitigating the sea-based Infrared search and track (IRST) problem. It is easy to detect small targets in homogeneous backgrounds such as the sky. When targets are on the border line of heterogeneous backgrounds such as the horizon in the sky and sea surface, solving the problem of detection becomes difficult. This paper presents a novel spatial filtering method, called Double Layered-Background Removal Filter (DL-BRF), for achieving high detection rates and low false alarm rates. DL-BRF consists of a Modified-Mean Subtraction Filter (M-MSF) and a consecutive LocalDirectional Background Removal Filter (L-DBRF). M-MSF enhances the target signal and reduces background noise. L-DBRF removes horizontal structures, which upgrade the signal-to-clutter ratio and background suppression factor. L-DBRF used after M-MSF enhances the synergistic performance of horizontal target detection. Additionally, the adaptive Hysteresis threshold-based scheme is a suitable detection method. We validate the superior performance of the proposed method via three types of evaluation tests, including a real test scenario.

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Section: Target Information-based Target Detectionmentioning

confidence: 99%

Double Layered-Background Removal Filter for Detecting Small Infrared Targets in Heterogenous Backgrounds

Kim

2010

J Infrared Milli Terahz Waves

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“…• Speed Tuning: The speed tuning transformation, denoted can be seen as two independent scaling operations performed on the spatial and temporal variables, i.e., (12) where and are such that the operator is unitary and maps the -plane into the -plane. These constraints result in two linear equations on and as follows.…”

Section: A Function Transformationsmentioning

confidence: 99%

“…A particular implementation of this approach can be found in [11], where first order differences from three consecutive image pairs are thresholded and the results merged to generate what is called the moving target indicator. A recursive implementation of the moving target indication algorithm is presented in [12].…”

Section: Introductionmentioning

confidence: 99%

A new motion parameter estimation algorithm based on the continuous wavelet transform

Mujica

Leduc

Murenzi

et al. 2000

IEEE Trans. on Image Process.

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This paper presents a novel motion parameter estimation (ME) algorithm based on the spatio-temporal continuous wavelet transform (CWT). The multidimensional nature of the CWT allows for the definition of a multitude of energy densities by integrating over a subset of the CWT parameter space. Three energy densities are used to estimate motion parameters by sequentially optimizing a state vector composed of velocity, position, and size parameters. This optimization is performed on a frame-by-frame basis allowing the algorithm to track moving objects. The ME algorithm is designed to address real world challenges encountered in the defense industry and traffic monitoring scenarios, such as attaining robust performance in noise and handling obscuration and crossing object trajectories.

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“…Post-processing of sensor information is also very important for better detection [1,2]. In case of the moving target, the inter-frame image di erence is very important [3][4][5]. For the target decision, such detected features are maximally utilized to recognize the target.…”

Section: Introductionmentioning

confidence: 99%

Automatic detection and tracking of moving image target with CNN‐UM via target probability fusion of multiple features

Kim¹,

Roska²,

Chua³

et al. 2003

Circuit Theory & Apps

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SUMMARYA high speed target detection and tracking algorithm for a CNN-UM chip is presented in this paper. The target conÿdence value is computed based on the fusion of target existence probabilities of features using products of weighted sums. The target decision is done with such a conÿdence value and target initiation is done through the temporal accumulation of the conÿdence. The probability of the target existence for each feature is created in the region of in uence depending on the reliability and the strength of the feature. By virtue of the analogic parallel processing structure of the CNN-UM (Roska T, Chua LO. The CNN universal machine: an analogic array computer. IEEE Trans. Circuits Systems II 1993; CAS-40:163-173), real time tracking can be achieved with presently available technologies with the speed of several kilo-frames per second. Due to the utilization of multiple features of target, robust target detection is possible via the proposed algorithm. On-chip experiments of the proposed targettracking algorithm have been done and properties of the proposed approach are disclosed through the various experiments.

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A recursive moving-target-indication algorithm for optical image sequences

Cited by 120 publications

References 14 publications

Double Layered-Background Removal Filter for Detecting Small Infrared Targets in Heterogenous Backgrounds

Double Layered-Background Removal Filter for Detecting Small Infrared Targets in Heterogenous Backgrounds

A new motion parameter estimation algorithm based on the continuous wavelet transform

Automatic detection and tracking of moving image target with CNN‐UM via target probability fusion of multiple features

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