Assessing Rotation-Invariant Feature Classification for Automated Wildebeest Population Counts

Torney, Colin J.; Dobson, Andrew; Borner, Felix; Lloyd‐Jones, David J.; Moyer, David; Maliti, Hanori; Mwita, Machoke; Fredrick, Howard; Borner, Markus; Hopcraft, J. Grant C.

doi:10.1371/journal.pone.0156342

Cited by 28 publications

(27 citation statements)

References 29 publications

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“…In some cases, it may be possible to reduce commission error rates to the point where manual review of automated analysis results is not necessary, while in other cases, like ours, varying degrees of manual postanalysis effort may be required. Although animal contrast in thermal-infrared imagery has proven useful for automated detection of mammals (Conn et al 2014, Chrétien et al 2015, 2016, Seymour et al 2017, the very coarse pixel resolution of thermal cameras compared to RGB cameras generally renders them ineffective for aerial detection of comparatively smaller birds (Chabot and Francis 2016). It should be noted that any aerial image-based survey will only allow detection of subjects that are visible from overhead and miss subjects that are, for example, concealed under canopy or diving underwater.…”

Section: Discussionmentioning

confidence: 99%

An approach for using off-the-shelf object-based image analysis software to detect and count birds in large volumes of aerial imagery

Chabot¹,

Dillon²,

Francis³

2018

ACE

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ABSTRACT. Computer-automated image analysis techniques can save time and resources for detecting and counting birds in aerial imagery. Sophisticated object-based image analysis (OBIA) software is now widely available and has proven effective for various challenging detection tasks, but there is a need to develop accessible and readily adaptable procedures that can be implemented in an operational context. We developed a systematic, repeatable approach using commercial off-the-shelf OBIA software, and tested its effectiveness and efficiency to detect and count Lesser Snow Geese (Chen caerulescens caerulescens) in large numbers of images of breeding colonies across the Canadian Arctic that present a variety of landscapes, numerous confounding features, and varying illumination conditions and exposure levels. Coarse-scale review of analysis results was necessary to remove conspicuous clusters of commission errors, thus rendering the technique semiautomated. It was effective for imagery with spatial resolutions of 4-5 cm, producing overall accurate estimates of goose numbers compared to manual counts (R2 = 0.998, regression coefficient = 0.974) in 41 test images drawn from several breeding colonies. The total automated count (19,920) across all test images exceeded the manual count (19,836) by just 0.4%. We estimate the typical time required to review images for errors to be only 5-10% of that required to count birds manually. This could reduce the person-time required to analyze aerial photos of the major Arctic colonies of Snow Geese from several months to several days. Our approach could be adapted to many other bird detection tasks in aerial imagery by anyone possessing at least basic skills in image analysis and geographic information systems.

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Section: Discussionmentioning

confidence: 99%

An approach for using off-the-shelf object-based image analysis software to detect and count birds in large volumes of aerial imagery

Chabot¹,

Dillon²,

Francis³

2018

ACE

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“…(a) Background subtraction of video frames yields the desired motion object (Weinstein, ) based on changes in past pixel values. (b) Counting wildebeest from imagery captured by unmanned aerial vehicle in Tanzania (Torney et al., ). The left panel are correct identifications of wildebeest, the right panel are false positives caused by a flock of juvenile ostrich.…”

Section: Countingmentioning

confidence: 99%

“…Chabot and Francis () reported that automated counts of waterbirds were within 3%–5% of human counts across 16 applications. Recent improvements of UAV‐based counting include utilizing hyperspectral data (Beijboom et al., ; Witharana & Lynch, ), pixel‐shape modelling (Liu et al., ) and combining background subtraction with machine learning (Torney et al., ) (Figure b). Recent efforts to count animals use deep learning neural networks are promising, but require tens of thousands of training images gathered by human annotation (Bowley et al., ).…”

Section: Countingmentioning

confidence: 99%

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A computer vision for animal ecology

Weinstein

2017

Journal of Animal Ecology

316

287

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A central goal of animal ecology is to observe species in the natural world. The cost and challenge of data collection often limit the breadth and scope of ecological study. Ecologists often use image capture to bolster data collection in time and space. However, the ability to process these images remains a bottleneck. Computer vision can greatly increase the efficiency, repeatability and accuracy of image review. Computer vision uses image features, such as colour, shape and texture to infer image content. I provide a brief primer on ecological computer vision to outline its goals, tools and applications to animal ecology. I reviewed 187 existing applications of computer vision and divided articles into ecological description, counting and identity tasks. I discuss recommendations for enhancing the collaboration between ecologists and computer scientists and highlight areas for future growth of automated image analysis.

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“…Maussang et al (2015) proposed general-purpose algorithms for detecting miscellaneous marine animals in ocean-surface images. Additional aerial image analysis techniques used to detect mammals have been reported by Sirmacek et al (2012), van Gemert et al (2015, Terletzky andRamsey (2016), andTorney et al (2016).…”

Section: Automated Bird Counts In Aerial Imagesmentioning

confidence: 99%

Computer-automated bird detection and counts in high-resolution aerial images: a review

2016

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Bird surveys conducted using aerial images can be more accurate than those using airborne observers, but can also be more time‐consuming if images must be analyzed manually. Recent advances in digital cameras and image‐analysis software offer unprecedented potential for computer‐automated bird detection and counts in high‐resolution aerial images. We review the literature on this subject and provide an overview of the main image‐analysis techniques. Birds that contrast sharply with image backgrounds (e.g., bright birds on dark ground) are generally the most amenable to automated detection, in some cases requiring only basic image‐analysis software. However, the sophisticated analysis capabilities of modern object‐based image analysis software provide ways to detect birds in more challenging situations based on a variety of attributes including color, size, shape, texture, and spatial context. Some techniques developed to detect mammals may also be applicable to birds, although the prevalent use of aerial thermal‐infrared images for detecting large mammals is of limited applicability to birds because of the low pixel resolution of thermal cameras and the smaller size of birds. However, the increasingly high resolution of true‐color cameras and availability of small unmanned aircraft systems (drones) that can fly at very low altitude now make it feasible to detect even small shorebirds in aerial images. Continued advances in camera and drone technology, in combination with increasingly sophisticated image analysis software, now make it possible for investigators involved in monitoring bird populations to save time and resources by increasing their use of automated bird detection and counts in aerial images. We recommend close collaboration between wildlife‐monitoring practitioners and experts in the fields of remote sensing and computer science to help generate relevant, accessible, and readily applicable computer‐automated aerial photographic census techniques.

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Assessing Rotation-Invariant Feature Classification for Automated Wildebeest Population Counts

Cited by 28 publications

References 29 publications

An approach for using off-the-shelf object-based image analysis software to detect and count birds in large volumes of aerial imagery

An approach for using off-the-shelf object-based image analysis software to detect and count birds in large volumes of aerial imagery

A computer vision for animal ecology

Computer-automated bird detection and counts in high-resolution aerial images: a review

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