A computer vision approach to classification of birds in flight from video sequences

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%

Improving the efficiency and accuracy of nocturnal bird Surveys through equipment selection and partial automation

This thesis was submitted for the degree of Engineering Doctorate and awarded by Brunel University.Birds are a key environmental asset and this is recognised through comprehensive legislation and policy ensuring their protection and conservation. Many species are active at night and surveys are required to understand the implications of proposed developments such as towers and reduce possible conflicts with these structures. Night vision devices are commonly used in nocturnal surveys, either to scope an area for bird numbers and activity, or in remotely sensing an area to determine potential risk. This thesis explores some practical and theoretical approaches that can improve the accuracy, confidence and efficiency of nocturnal bird surveillance. As image intensifiers and thermal imagers have operational differences, each device has associated strengths and limitations. Empirical work established that image intensifiers are best used for species identification of birds against the ground or vegetation. Thermal imagers perform best in detection tasks and monitoring bird airspace usage. The typically used approach of viewing bird survey video from remote sensing in its entirety is a slow, inaccurate and inefficient approach. Accuracy can be significantly improved by viewing the survey video at half the playback speed. Motion detection efficiency and accuracy can be greatly improved through the use of adaptive background subtraction and cumulative image differencing. An experienced ornithologist uses bird flight style and wing oscillations to identify bird species. Changes in wing oscillations can be represented in a single inter-frame similarity matrix through area-based differencing. Bird species classification can then be automated using singular value decomposition to reduce the matrices to one-dimensional vectors for training a feed-forward neural network

Improving the efficiency and accuracy of nocturnal bird Surveys through equipment selection and partial automation

Birds are a key environmental asset and this is recognised through comprehensive legislation and policy ensuring their protection and conservation. Many species are active at night and surveys are required to understand the implications of proposed developments such as towers and reduce possible conflicts with these structures. Night vision devices are commonly used in nocturnal surveys, either to scope an area for bird numbers and activity, or in remotely sensing an area to determine potential risk. This thesis explores some practical and theoretical approaches that can improve the accuracy, confidence and efficiency of nocturnal bird surveillance. As image intensifiers and thermal imagers have operational differences, each device has associated strengths and limitations. Empirical work established that image intensifiers are best used for species identification of birds against the ground or vegetation. Thermal imagers perform best in detection tasks and monitoring bird airspace usage. The typically used approach of viewing bird survey video from remote sensing in its entirety is a slow, inaccurate and inefficient approach. Accuracy can be significantly improved by viewing the survey video at half the playback speed. Motion detection efficiency and accuracy can be greatly improved through the use of adaptive background subtraction and cumulative image differencing. An experienced ornithologist uses bird flight style and wing oscillations to identify bird species. Changes in wing oscillations can be represented in a single inter-frame similarity matrix through area-based differencing. Bird species classification can then be automated using singular value decomposition to reduce the matrices to one-dimensional vectors for training a feed-forward neural network.EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Classification of bird species from video using appearance and motion features

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-of-the-art Deep Convolutional Neural Networks (CNNs). Using our algorithm we achieved a 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

Estimating bird flight height using 3-D photogrammetry

Harnessing wind or solar power have become popular “green” options for energy production. However, colliding with wind turbine blades or being burned by concentrated solar flux around power towers can present a substantial threat to birds. Assessing the severity of this risk to different bird species requires accurate estimates of their flight height. We developed a three-dimensional (3-D) stereophotogrammetric approach to determine bird flight heights. The accuracy of four varying stereophotogrammetric camera layouts was compared between each other and against laser-based rangefinder measurements of static structures. Bird flight heights were measured and compared between species, and repetitive photographic captures over short time periods were tested for autocorrelation. Three out of four camera layouts performed equally well when measuring static structures at distances of up to 100 m (0.0 ± 0.3%; or 0.00 ± 0.03 m error), better than laser-based rangefinders (0.3 ± 4.8%; or 0.12 ± 0.51 m error) on a small target. Photogrammetrically measured flight heights were precise to 0.07 ± 0.05 m up to ~275 m away and to within 1 m at 400 m, and measurable up to ~535 m away. Using this tested approach, repetitive, sequential flight heights of moving birds were significantly autocorrelated compared to random flight heights (P = 0.001). Species-specific flight heights were distinct, practically demonstrating the approach’s potential application, however, scarcity of flight height data prompts further application of the approach to record distributions of flight height. This stereophotogrammetric method was accurate, cost-effective, objective, and relatively simple to apply. It could measure flight heights, and potentially micro-avoidance behaviour in 3-D flight patterns, to ultimately identify species that are at potential risk of collision or burning with wind turbines and solar towers.SUPPORTING INFORMATION: FIGURE S1. Three-dimensional representation of a sacred ibis flight path in relation to a floodlight (top and side view, respectively). Using the times associated with each set of photographs and the calculated distance between each flight point, the velocity of the bird can be calculated.APPENDIX S1. Trigonometric calculations of the horizontal and vertical area covered by the overlapping fields of view. The maximum measurable flight height was determined for different camera configurations, adjusted for spacing and forward rotation between cameras, and varying distance between the central camera and the focal structure.Tshwane Technology Innovation Agency and Hans Hoheisen Charitable Trust.https://zslpublications.onlinelibrary.wiley.com/journal/14697998hj2022Mammal Research InstituteZoology and Entomolog

Offshore Wind Energy and Seabird Collision Vulnerability in California

California has ambitious clean energy goals designed to help mitigate the worst outcomes of climate change. Offshore wind is an important part of the solution to meet California’s clean energy goals, but has potential negative impacts on the marine environment. As offshore wind energy is new to California, this paper reviews and synthesizes existing literature from other parts of the world, looking at the real and theoretical risk of seabird collision with offshore wind turbines. Learnings from existing offshore wind projects in the U.K. as well as theoretical and modeled risk assessments are applied to California’s plans to determine the risk of seabird collision with turbines in the Humboldt and Morro Bay Wind Energy Areas. The species groups most vulnerable to collision are pelicans, terns, albatross, medium and large gulls, sea ducks, phalaropes, and jaegers/skuas. Generally, across real surveys of operating wind farms and models, collision risk is low. Seabirds tend to avoid wind energy areas completely, flying around the wind energy area at a distance. Those birds who enter the wind energy areas can most often avoid turbines by flying between them. Despite the overall low risk, several key factors impact a bird’s ability to avoid turbines: placement of turbines and wind energy areas in relation to breeding or roosting grounds and foraging locations, visibility conditions, artificial lights, and attraction to the wind energy area through food availability and roosting opportunities. More information on region specific flight behavior, migration and commuting routes, and avoidance rates should be collected in the California wind energy areas before turbines are deployed to better anticipate and avoid risk