Wind farm and fauna interaction: detecting bird and bat wing beats through cyclic motion analysis

Lazarevic, Ljubica; Harrison, David; Southee, Darren; Wade, M.; Osmond, John

doi:10.1080/19397030802221273

Cited by 4 publications

(4 citation statements)

References 22 publications

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“…The periodic motion features associated with beating wings vary among species (Lazarevic et al, 2008), and may provide a useful discriminating feature for classification. This includes both short-scale features (frequency while flapping), but also others: for example some species characteristically mix flapping and gliding.…”

Section: Wing Beat Frequency Based Featuresmentioning

confidence: 99%

Automatic classification of flying bird species using computer vision techniques

Atanbori

Duan

Murray

et al. 2016

Pattern Recognition Letters

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Bird populations are identified as important biodiversity indicators, so collecting reliable population data is important to ecologists and scientists. However, existing manual monitoring methods are labour-intensive, time-consuming, and potentially error prone. The aim of our work is to develop a reliable automated system, capable of classifying the species of individual birds, during flight, using video data. This is challenging, but appropriate for use in the field, since there is often a requirement to identify in flight, rather than while stationary. We present our work, which uses a new and rich set of appearance features for classification from video. We also introduce motion features including curvature and wing beat frequency. Combined with Normal Bayes classifier and a Support Vector Machine classifier, we present experimental evaluations of our appearance and motion features across a data set comprising 7 species. Using our appearance feature set alone we achieved a classification rate of 92% and 89% (using Normal Bayes and SVM classifiers respectively) which significantly outperforms a recent comparable state-of-the-art system. Using motion features alone we achieved a lower-classification rate, but motivate our on-going work which we seeks to combine these appearance and motion feature to achieve even more robust classification.

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Section: Wing Beat Frequency Based Featuresmentioning

confidence: 99%

Automatic classification of flying bird species using computer vision techniques

Atanbori

Duan

Murray

et al. 2016

Pattern Recognition Letters

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“…The periodicity of wings beats is known to vary among bird species (Lazarevic et al, 2008). In Atanbori et al (2013) we show that for bat species, a bounding box tted to the silhouette of a tracked individual can be used to measure the periodicity of wing beats.…”

Section: Wing Beat Frequency Featuresmentioning

confidence: 99%

“…For example, Cullinan et al (2015); Matzner et al (2015); Hristov et al (2010); Betke et al (2008); Lazarevic et al (2008) censused large bat populations. Betke et al (2008) also estimated wing beat frequencies of individuals using pose templates, and applying Fast Fourier Transform (FFT).…”

Section: Studies Related To Other Speciesmentioning

confidence: 99%

Classification of bird species from video using appearance and motion features

Atanbori

Duan

Shaw³

et al. 2018

Ecological Informatics

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The monitoring of bird populations can provide important information on the state of sensitive ecosystems; however, the manual collection of reliable population data is labour-intensive, time-consuming, and potentially error prone. Automated monitoring using computer vision is therefore an attractive proposition, which could facilitate the collection of detailed data on a much larger scale than is currently possible.A number of existing algorithms are able to classify bird species from individual high quality detailed images often using manual inputs (such as a priori parts labelling). However, deployment in the field necessitates fully automated in-flight classification, which remains an open challenge due to poor image quality, high and rapid variation in pose, and similar appearance of some species. We address this as a fine-grained classification problem, and have collected a video dataset of thirteen bird classes (ten species and another with three colour variants) for training and evaluation. We present our proposed algorithm, which selects effective features from a large pool of appearance and motion features. We compare our method to others which use appearance features only, including image classification using state-ofthe-art Deep Convolutional Neural Networks (CNNs). Using our algorithm we achieved an 90% correct classification rate, and we also show that using effectively selected motion and appearance features together can produce results which outperform state-of-the-art single image classifiers. We also show that the most significant motion features improve correct classification rates by 7% compared to using appearance features alone.

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“…The periodic motion features associated with wing beats vary among species [15] and provide useful discriminating features for classification. We used the approach proposed by Atanbori et al [2], by fitting a bounding box to the silhouette of a tracked bird and extracted the bounding box's height, width and diagonal as three different 1-dimensional (1 D) time signal.…”

Section: Motion Featuresmentioning

confidence: 99%

A Computer Vision Approach to Classification of Birds in Flight from Video Sequences

Atanbori¹,

Duan²,

Murray³

et al. 2015

Procedings of the Machine Vision of Animals and Their Behaviour Workshop 2015

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Bird populations are an important bio-indicator; so collecting reliable data is useful for ecologists helping conserve and manage fragile ecosystems. However, existing manual monitoring methods are labour-intensive, time-consuming, and error-prone. The aim of our work is to develop a reliable system, capable of automatically classifying individual bird species in flight from videos. This is challenging, but appropriate for use in the field, since there is often a requirement to identify in flight, rather than when stationary. We present our work in progress, which uses combined appearance and motion features to classify and present experimental results across seven species using Normal Bayes classifier with majority voting and achieving a classification rate of 86%.

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Wind farm and fauna interaction: detecting bird and bat wing beats through cyclic motion analysis

Cited by 4 publications

References 22 publications

Automatic classification of flying bird species using computer vision techniques

Automatic classification of flying bird species using computer vision techniques

Classification of bird species from video using appearance and motion features

A Computer Vision Approach to Classification of Birds in Flight from Video Sequences

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