Detection of small moving objects in image sequences using multistage hypothesis testing

Blostein, Steven D.; Huang, Thomas S.

doi:10.1109/icassp.1988.196779

Cited by 22 publications

(4 citation statements)

References 10 publications

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“…The method of Multistage Hypothesizing Testing (MHT) [2] treats a number of candidate trajectories as a tree-structure. The pixels along a trajectory are tested sequentially for a shift in mean intensity using MHT which is designed according to pre-specified error probabilities.…”

Section: Introductionmentioning

confidence: 99%

Space multi-target detection by second variance

Huang

et al. 2013

Proceedings 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC)

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Detecting small multi-target in aerospace is both a significant and difficult issue for the satellite tracking system. It is fundamentally challenging due to the existence of strong noise in captured images, the few characteristic of spot-like target, the simultaneous multiple targets and the real-time requirement. To address these challenges, we formulate space multi-target as a two-step detection problem, which integrates the variance vector detection and 2th variance detection. The pixels of images are projected to the variance subspace of two-dimension. In the first step, the candidate targets are extracted with the optimal threshold achieved by K-means and proposed Weighted Maximum Right Probability, namely WMRP. In the second step, the true targets are checked out by adopting the proposed 2th variance feature and multiscale threshold. Experiments demonstrate the proposed two-step framework can efficiently and rapidly detect the small multiple targets, which can satisfy the common application of space multi-target tracking.

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Section: Introductionmentioning

confidence: 99%

Space multi-target detection by second variance

Huang

et al. 2013

Proceedings 2013 International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC)

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“…The method based on target characteristic use one kind characteristic of the target, such as space distribution on grey value, change on time, move characteristic, and so on. The method include 3D matched filter [5] , multiple hypothesis testing [6] , dynamic programming [7] , high order correlation [8] , and so on.…”

Section: Introductionmentioning

confidence: 99%

Modify model for infrared dim target detection

Zhang

et al. 2011

SPIE Proceedings

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More and more infrared imaging systems were applied in military and civil fields due to the ability of detecting in night. Performance of detecting dim target is one important index for infrared imaging system. But it is difficult to detect dim target from infrared image due to weak in luminous and small in sizes. An important approach based on background suppression was used in many applications for it was effective in most applications and computed easily and quickly. But the performance of target detection were descended with many numbers of false alarms when there exists noise pixels in infrared image. In order to resolve the problem induced by noise pixels, in this paper a modify detection model wasproposed. Modify detection model use two step filter processes to suppress noise and estimate background image. After selecting the first filter operator, a detection system base on modify detection model was realized. Experiment resultsshow that modify detection model improve target detection ratio and reduce the numbers of false alarm on noisy infrared images.

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“…Recently, the detection of moving small dim targets under heavy IR background clutter has been an active research area, and many algorithms for this problem have been developed. [1][2][3][4] The algorithms used in IR searching and tracking systems are adequate for applications with bright targets against background clutter, and make use of only the spatial information of the targets and clutter without considering their temporal behavior. Some new approaches 2,3 incorporated temporal and spatial information have good performance for the detection of small dim targets in IR image sequences, but heavy computational complexity is a frustration in practical applications.…”

Section: Introductionmentioning

confidence: 99%

Improved power-law-detector-based moving small dim target detection in infrared images

2008

Opt. Eng.

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A novel algorithm based on an improved powerlaw detector is proposed to detect moving small dim targets against heavy cluttered background in IR image sequences. As the gray-scale value of a pixel in an image fluctuates when a target passes by, this fluctuation can be viewed as a weak transient signal, and can be detected by the power-law detector, which performs well for transient signal detection. The experimental results illustrate that the proposed algorithm is effective and adaptable for moving small dim target detection in image sequences with heavy clutter.

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Detection of small moving objects in image sequences using multistage hypothesis testing

Cited by 22 publications

References 10 publications

Space multi-target detection by second variance

Space multi-target detection by second variance

Modify model for infrared dim target detection

Improved power-law-detector-based moving small dim target detection in infrared images

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