Keywords

aerial imagery, local feature extraction, probability density function, mean-shift segmentation, graph theory, graph-cut, animal detection, animal counting

Start Date

1-7-2012 12:00 AM

Description

For effective conservation management, it is very important to provide accurate estimates of animal populations with certain time intervals. So far many studies are performed visually/manually which requires much time and is prone to errors. Besides, only a limited area can be considered for counting because of the effort required. In order to bring a new solution to this problem, herein we propose a novel approach for counting animals from aerial images by using computer vision techniques. To do so, we apply a probabilistic framework on local features in the image whose pectral reflectance differs from the surrounding region. We use mean shift segmentation and obtain probability density function (pdf) to detect focus of attention regions (FOA). Finally, we benefit from graph theory to detect segments which should represent animals. We test the feasibility of the proposed approach using aerial images of varying quality and angles (including orthogonal time lapse photography) from several different terrestrial ecosystems. Monitored species include birds and mammals. The algorithms successfully detect and count animals and provide a replicable and objective method for estimating animal abundance, however the methodology still requires estimates of error to be incorporated. This approach highlights how technical innovations in remote sensing can provide valuable information for conservation management.

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Jul 1st, 12:00 AM

Automatic population counts for improved wildlife management using aerial photography

For effective conservation management, it is very important to provide accurate estimates of animal populations with certain time intervals. So far many studies are performed visually/manually which requires much time and is prone to errors. Besides, only a limited area can be considered for counting because of the effort required. In order to bring a new solution to this problem, herein we propose a novel approach for counting animals from aerial images by using computer vision techniques. To do so, we apply a probabilistic framework on local features in the image whose pectral reflectance differs from the surrounding region. We use mean shift segmentation and obtain probability density function (pdf) to detect focus of attention regions (FOA). Finally, we benefit from graph theory to detect segments which should represent animals. We test the feasibility of the proposed approach using aerial images of varying quality and angles (including orthogonal time lapse photography) from several different terrestrial ecosystems. Monitored species include birds and mammals. The algorithms successfully detect and count animals and provide a replicable and objective method for estimating animal abundance, however the methodology still requires estimates of error to be incorporated. This approach highlights how technical innovations in remote sensing can provide valuable information for conservation management.