Analysis of spatial point process characteristics of radar detections in sea clutter region

Baykut, Suleyman; Ozcan, Abdullah H.; Sahinkaya, Demet S. Armagan; Yalcin, Ilhan K.

doi:10.1109/siu.2013.6531418

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…The interested reader may find a review of point pattern analysis in ecological studies [12]. Point pattern statistics has also been used in radar applications [3]. In this study, we take the location of a person that is detected from airborne image as object of interest.…”

Section: Introductionmentioning

confidence: 99%

Sparse people group and crowd detection using spatial point statistics in airborne images

Ozcan

Ünsalan

Reinartz

2015

2015 7th International Conference on Recent Advances in Space Technologies (RAST)

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Crowd monitoring is an important task of security forces. If an emergency occurs during large events, authorities should take urgent measures to prevent causalities. Also understanding crowd dynamics such as tracking crowds or sparse people goups before an emergency occurs is a need. Therefore, crowd detection and analysis is a critical research area. There are several studies for crowd monitoring that use street or indoor cameras which may not be directly used for analyzing large crowds. In this study, we approach the problem using aerial images. We propose two novel methods. In the first method, we use first-order spatial point statistics. It uses the nearest neighbor relations for each person in the image to detect crowd regions. Our second method also uses the first order statistics with an additional sparse people group detection flexibility. We test the proposed methods on two aerial images and provide quantitative test results.

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Section: Introductionmentioning

confidence: 99%

Sparse people group and crowd detection using spatial point statistics in airborne images

Ozcan

Ünsalan

Reinartz

2015

2015 7th International Conference on Recent Advances in Space Technologies (RAST)

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“…In this study, we propose two novel methods for crowd detection in aerial images. These are based on spatial point pattern statistics [3]. Specifically, we benefit from the first and second order statistics.…”

Section: Introductionmentioning

confidence: 99%

Crowd detection in airborne images using spatial point statistics

Ozcan

Ünsalan

Reinartz

2015

2015 23nd Signal Processing and Communications Applications Conference (SIU)

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The crowd density in public places increases in social events. If an emergency occurs during such events, authorities should take urgent measures to prevent causalities. Therefore, crowd detection and analysis is a critical research area. Even though there are several studies on person detection from street or indoor cameras, these may not be directly used to detect or analyze the crowd formed from people. In this study, we approach the problem using aerial images. We propose two novel methods to detect the crowd using spatial statistics. The first novel method is based on the first-order statistics. It uses the nearest neighbor relations for each person in the image. The second novel method is based on the second-order statistics. Here, the spatial position of persons are checked whether they are clustered or randomly distributed. We test these two methods on a sample test image and provide performance measures.

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Analysis of spatial point process characteristics of radar detections in sea clutter region

Cited by 2 publications

References 4 publications

Sparse people group and crowd detection using spatial point statistics in airborne images

Sparse people group and crowd detection using spatial point statistics in airborne images

Crowd detection in airborne images using spatial point statistics

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