The Detection And Tracking Of Multiple Targets With Velocity Filters

Kendall, William B.; Jacobi, William J.

doi:10.1117/12.951660

Cited by 4 publications

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“…al [2] and Porat and Friedlander [3] proposed the method using 3-dimensional Fourier transform. Kendall and Jacobi [4] and Fleet and Jepson [5] proposed the method using velocity filter bank. Blostein and Huang [1] proposed a hypothesis test method by storing every string of pixels that corresponds to all target trajectories in a lookup table of the tree structure.…”

Section: Introductionmentioning

confidence: 99%

“…Most of them process image sequences on the assumption that the targets move with constant velocities [1][2][3][4][5]. Mahmoud et.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, there has been a growing interest in detection of small low-contrast objects from optical or infrared (IR) image sequences [1][2][3][4][5][6][7][8][9][10][11][12]. The problem of detecting small low-contrast objects arises, for example, when we detect small moving targets from an on-board JR sensor [6,12], and when we detect meteors, asteroids and satellites in the night sky from an array of charge coupled device (CCD) sensors on the focal plane of a telescope [1,8,11].…”

Section: Introductionmentioning

confidence: 99%

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<title>Small target detection from image sequences using recursive max filter</title>

1995

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This paper presents a new method for detection of small moving targets from noisy image sequences using a recursive filter, which contains a local maximum filter in the feedback loop. Since all the procedures contained in this method can be implemented by fully parallel algorithm, this method can be realized by a neural network and real-time processing is possible for image sequences at high frame rates. First we investigate the performance of the method on the assumption that the targets move randomly in an image plane and the background noise is white both in time and space. The results show that the proposed method has the ability to suppress the background noise and enhances small moving targets, and that the targets are detected only by thresholding after the processing of the recursive filter. There can be other backgrounds such as clutters and stationary objects. It is also shown that the above method is applicable after preprocessing of conventional time difference method, and is able to suppress these backgrounds. Next we analyze the performance of the method using the property of order statistics. The performance measure is the output signal-to-noise ratio (SNR) and is represented analytically as a function of input SNR and the parameters included in the method. Finally, the recursive filter is optimized using the result of the analysis.

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Section: Introductionmentioning

confidence: 99%

“…Most of them process image sequences on the assumption that the targets move with constant velocities [1][2][3][4][5]. Mahmoud et.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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<title>Small target detection from image sequences using recursive max filter</title>

1995

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“…Recently-developed signal processing algorithms utilizing multi-frame track-before-detect or velocity filtering techniques provide optimum or near-optimum detection as well as robust track initiation for both single and multiple targets [1][2][3][4] . Since these algorithms are computationally very intensive, however, their use has generally not been practical for realtime applications, especially in cases where there are stringent limitations on size, weight and power (as for example in tactical missile guidance or space-based surveillance).…”

Section: Introductionmentioning

confidence: 99%

<title>Miniaturized real-time processor for image sequence analysis</title>

1990

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Powerful new signal processing algorithms are now becoming available for such tasks as the detection and tracking of multiple targets via image se quence analysis. For typical real-time applications, these algorithms require throughputs on the order of hundreds to thousands of MFLOPS. In order to achieve such throughputs, it is necessary to employ parallel processing architectures, which normally require large size, weight and power consumption for their implementation. We have developed a high-performance, programmable MIMD processor called the SCC-lOO which is particularly well-suited for miniaturization using hybrid wafer-scale packaging technology. A 20-node configuration with a peak throughput in excess of 1 GFLOPS will be packaged in a three-inch cube using this approach.

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Detection and tracking of single-pixel targets based on trajectory continuity

Wang¹,

Inigo

1993

Image and Vision Computing

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The Detection And Tracking Of Multiple Targets With Velocity Filters

Cited by 4 publications

References 0 publications

<title>Small target detection from image sequences using recursive max filter</title>

<title>Small target detection from image sequences using recursive max filter</title>

<title>Miniaturized real-time processor for image sequence analysis</title>

Detection and tracking of single-pixel targets based on trajectory continuity

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