The enhancement and tracking of moving objects in digital images using adaptive three-dimensional recursive filters

Bruton, L.T.; Bartley, N.R.

doi:10.1109/tcs.1986.1085971

Cited by 41 publications

(12 citation statements)

References 5 publications

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“…Bruton and Bartley [9] have shown that 3-D recursive digital linear-trajectory (LT) filters may be used to selectively track the motion of objects in digital image sequences. Their method adaptively steers the 3-D frequency-planar passband of the filter to track the slowly time-varying, 3-D frequency-planar energy density function of the moving object in space-time.…”

Section: Tracking Of Dynamic Objectsmentioning

confidence: 99%

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Dynamic image sequence analysis using fuzzy measures

Liu

Bruton²,

Bezdek³

et al. 2001

IEEE Trans. Syst., Man, Cybern. B

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In this paper, we present an image understanding system using fuzzy sets and fuzzy measures. This system is based on a symbolic object-oriented image interpretation system. We apply a simple, powerful three-dimensional (3-D) recursive filter to tracking moving objects in a dynamic image sequence. This filter has a time-varying 3-D frequency-planar passband that is adapted in a feedback system to automatically track moving objects. However, as objects in the image sequence are not well-defined and are engaged in dynamic activities, their shapes and trajectories in most cases can be described only vaguely. In order to handle these uncertainties, we use fuzzy measures to capture subtle variations and manage the uncertainties involved. This enables us to develop an image understanding system that produces a very natural output. We demonstrate the effectiveness of our system with complex real traffic scenes.

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Section: Tracking Of Dynamic Objectsmentioning

confidence: 99%

“…5. Following [9], we determine the position of the object at the output of the 3-D LT filter in each frame by computing the intensity centroid within an object window, as shown in Fig. 6.…”

Section: B Adaptive 3-d Recursive Tracking Filtermentioning

confidence: 99%

Dynamic image sequence analysis using fuzzy measures

Liu

Bruton²,

Bezdek³

et al. 2001

IEEE Trans. Syst., Man, Cybern. B

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“…The linear difference equation (LDE) method is proposed in [1]. The continuous 3-D Laplace transform transfer function is designed according to the trajectory of the target.…”

Section: Introductionmentioning

confidence: 99%

Tracking moving objects in image sequences using 1-D trajectory filter

Pei¹,

Kuo²,

Huang³

2006

IEEE Signal Process. Lett.

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The three-dimensional (3-D) Fourier transform of the nonaccelerating object in an image sequence lies on a plane passing through the origin of the 3-D frequency domain. Instead of using a 3-D frequency planar filter, we propose a new filter design technique for object tracking using a one-dimensional trajectory filter-in the spatiotemporal domain. This filter can track a moving object with linear or nonlinear trajectory without prior knowledge of the target trajectory. Also, it is easy to design and has a very low computational complexity. More importantly, it can be implemented in real time.

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“…Bruton and Bartley [19] developed a 3D resonant plane filter to track and enhance a moving target. This method required a relatively large target SNR to provide periodic estimates of the target position for the filter to track.…”

Section: Introductionmentioning

confidence: 99%

Analysis of multiframe target detection using pixel statistics

Wei

Zeidler

1995

IEEE Trans. Aerosp. Electron. Syst.

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Current track-before-detect (TBD) algorithms are developed and analyzed using a path statistic for each potential object trajectory. However this path statistic does not characterize overall performance gain. We propose a pixel-based statistic. This allows the TBD approach to be characterized as an image enhancement algorithm with detection gains compared with single frame detections. It is shown that for the TBD approach to have superior detection over single frame detection the target signal-to-noise ratio (SNR) must be greater than a threshold SNR in order to overcome the uncertainty in the target path. Tradeoffs are made for a class of velocity constrained target paths in terms of the detection gain with respect to the maximum target velocity and number of frames integrated.Manuscript

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The enhancement and tracking of moving objects in digital images using adaptive three-dimensional recursive filters

Cited by 41 publications

References 5 publications

Dynamic image sequence analysis using fuzzy measures

Dynamic image sequence analysis using fuzzy measures

Tracking moving objects in image sequences using 1-D trajectory filter

Analysis of multiframe target detection using pixel statistics

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