A high-accuracy, time-saving method for extracting nest watch data from video recordings

Evans, Dean R.; McArthur, Sarah L.; Bailey, Jacob; Church, John S.; Reudink, Matthew W.

doi:10.1007/s10336-015-1267-5

Cited by 4 publications

(1 citation statement)

References 19 publications

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“…This extraction capacity will be a key advance to enable data analysis of highspeed recordings from train fronts, similar to the extraction of useful biological monitoring data from continuous recording webcams or sensor networks (Benson et al, 2010;Porter et al, 2010;Evans et al, 2015). In our case, review of the recordings was focused on the points at which the cabin observer detected birds in front of the train, and on a small complementary sampling of the recordings as a whole.…”

Section: Discussionmentioning

confidence: 99%

On-Board Video Recording Unravels Bird Behavior and Mortality Produced by High-Speed Trains

et al. 2017

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Large high-speed railway (HSR) networks are planned for the near future to accomplish increased transport demand with low energy consumption. However, high-speed trains produce unknown avian mortality due to birds using the railway and being unable to avoid approaching trains. Safety and logistic difficulties have precluded until now mortality estimation in railways through carcass removal, but information technologies can overcome such problems. We present the results obtained with an experimental on-board system to record bird-train collisions composed by a frontal recording camera, a GPS navigation system and a data storage unit. An observer standing in the cabin behind the driver controlled the system and filled out a form with data of collisions and bird observations in front of the train. Photographs of the train front taken before and after each journey were used to improve the record of killed birds. Trains running the 321.7 km line between Madrid and Albacete (Spain) at speeds up to 250-300 km/h were equipped with the system during 66 journeys along a year, totaling approximately 14,700 km of effective recording. The review of videos produced 1,090 bird observations, 29.4% of them corresponding to birds crossing the infrastructure under the catenary and thus facing collision risk. Recordings also showed that 37.7% bird crossings were of animals resting on some element of the infrastructure moments before the train arrival, and that the flight initiation distance of birds (mean ± SD) was between 60 ± 33 m (passerines) and 136 ± 49 m (raptors). Mortality in the railway was estimated to be 60.5 birds/km year on a line section with 53 runs per day and 26.1 birds/km year in a section with 25 runs per day. Our results are the first published estimation of bird mortality in a HSR and show the potential of information technologies to yield useful data for monitoring the impact of trains on birds via on-board recording systems. Moreover, recordings point to the use of the infrastructure by birds as a key issue leading to bird train-kill.

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Section: Discussionmentioning

confidence: 99%

On-Board Video Recording Unravels Bird Behavior and Mortality Produced by High-Speed Trains

et al. 2017

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Efficacy of automated detection of motion in wildlife monitoring videos

et al. 2019

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Documenting biodiversity and wildlife behavior is time‐consuming and potentially invasive. Remotely placed cameras often are used to increase sampling and minimize disturbance of focal animals. Such wildlife monitoring programs can entail numerous and lengthy videos requiring massive amounts of time to analyze their content. We evaluated the efficacy of a computer program, MotionMeerkat (http://benweinstein.weebly.com/motionmeerkat.html), for automated detection of motion in 21 continuous, 3‐hour‐long videos monitoring woodpecker (Picidae) nest cavities during 2016 in Washington, USA. The program produces still‐image frame captures from the video where motion was detected, files logging the video time‐stamps of each frame, and x,y coordinates within each frame where motion was detected. We compared program results with video reviews conducted by students using a standard protocol for identifying movement events and species identification. The program tagged 60% more motion events of all types, and 38% more motion events of target species (woodpeckers and potential predators), than were found by student reviews. We also conducted sensitivity analyses of the program by varying program input settings; results suggest that optimal settings depend on the video quality, environmental conditions, desired target species, and type of motion. We conclude that automated motion‐detection programs such as MotionMeerkat can play important roles in use of continuous videos for wildlife monitoring, but results need to be reviewed by the researcher. © 2019 The Wildlife Society.

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The influence of weather and parental provisioning on fledging success depends on nest box type in a cavity-nesting passerine, the Mountain Bluebird (Sialia currucoides)

Leroux¹,

McKellar²,

Flood³

et al. 2018

The Wilson Journal of Ornithology

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A high-accuracy, time-saving method for extracting nest watch data from video recordings

Cited by 4 publications

References 19 publications

On-Board Video Recording Unravels Bird Behavior and Mortality Produced by High-Speed Trains

On-Board Video Recording Unravels Bird Behavior and Mortality Produced by High-Speed Trains

Efficacy of automated detection of motion in wildlife monitoring videos

The influence of weather and parental provisioning on fledging success depends on nest box type in a cavity-nesting passerine, the Mountain Bluebird (Sialia currucoides)

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