scholarly journals Innovations in Camera Trapping Technology and Approaches: The Integration of Citizen Science and Artificial Intelligence

Animals ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 132 ◽  
Author(s):  
Siân E. Green ◽  
Jonathan P. Rees ◽  
Philip A. Stephens ◽  
Russell A. Hill ◽  
Anthony J. Giordano
Keyword(s):  
Artificial Intelligence ◽  
Citizen Science ◽  
Public Involvement ◽  
Research Process ◽  
Camera Trap ◽  
Camera Traps ◽  
Camera Trapping ◽  
The Social ◽  
User Friendly

Camera trapping has become an increasingly reliable and mainstream tool for surveying a diversity of wildlife species. Concurrent with this has been an increasing effort to involve the wider public in the research process, in an approach known as ‘citizen science’. To date, millions of people have contributed to research across a wide variety of disciplines as a result. Although their value for public engagement was recognised early on, camera traps were initially ill-suited for citizen science. As camera trap technology has evolved, cameras have become more user-friendly and the enormous quantities of data they now collect has led researchers to seek assistance in classifying footage. This has now made camera trap research a prime candidate for citizen science, as reflected by the large number of camera trap projects now integrating public participation. Researchers are also turning to Artificial Intelligence (AI) to assist with classification of footage. Although this rapidly-advancing field is already proving a useful tool, accuracy is variable and AI does not provide the social and engagement benefits associated with citizen science approaches. We propose, as a solution, more efforts to combine citizen science with AI to improve classification accuracy and efficiency while maintaining public involvement.

The Science and Art of Interviewing

2020 ◽  
Author(s):  
Kathleen Gerson ◽  
Sarah Damaske
Keyword(s):  
Social Research ◽  
Research Question ◽  
Research Process ◽  
Social Contexts ◽  
Science And Art ◽  
The Social ◽  
Interview Guide ◽  
Hidden Patterns ◽  
User Friendly ◽  
Depth Interviews

Qualitative interviewing is one of the most widely used methods in social research, but it is arguably the least well understood. To address that gap, this book offers a theoretically rigorous, empirically rich, and user-friendly set of strategies for conceiving and conducting interview-based research. Much more than a how-to manual, the book shows why depth interviewing is an indispensable method for discovering and explaining the social world—shedding light on the hidden patterns and dynamics that take place within institutions, social contexts, relationships, and individual experiences. It offers a step-by-step guide through every stage in the research process, from initially formulating a question to developing arguments and presenting the results. To do this, the book shows how to develop a research question, decide on and find an appropriate sample, construct an interview guide, conduct probing and theoretically focused interviews, and systematically analyze the complex material that depth interviews provide—all in the service of finding and presenting important new empirical discoveries and theoretical insights. The book also lays out the ever-present but rarely discussed challenges that interviewers routinely encounter and then presents grounded, thoughtful ways to respond to them. By addressing the most heated debates about the scientific status of qualitative methods, the book demonstrates how depth interviewing makes unique and essential contributions to the research enterprise. With an emphasis on the integral relationship between carefully crafted research and theory building, the book offers a compelling vision for what the “interviewing imagination” can and should be.

scholarly journals Can citizen science analysis of camera trap data be used to study reproduction? Lessons from Snapshot Serengeti program

2020 ◽  
Author(s):  
Thel Lucie ◽  
Chamaillé-Jammes Simon ◽  
Keurinck Léa ◽  
Catala Maxime ◽  
Packer Craig ◽  
...  
Keyword(s):  
Citizen Science ◽  
Camera Trap ◽  
Camera Traps ◽  
Life History Trait ◽  
List Type ◽  
Breeding Phenology ◽  
Science Data ◽  
Morphological Criteria ◽  
Wide Range ◽  
Trained Observers

AbstractEcologists increasingly rely on camera trap data to estimate a wide range of biological parameters such as occupancy, population abundance or activity patterns. Because of the huge amount of data collected, the assistance of non-scientists is often sought after, but an assessment of the data quality is a prerequisite to their use.We tested whether citizen science data from one of the largest citizen science projects - Snapshot Serengeti - could be used to study breeding phenology, an important life-history trait. In particular, we tested whether the presence of juveniles (less than one or 12 months old) of three ungulate species in the Serengeti: topi Damaliscus jimela, kongoni Alcelaphus buselaphus and Grant’s gazelle Nanger granti could be reliably detected by the “naive” volunteers vs. trained observers. We expected a positive correlation between the proportion of volunteers identifying juveniles and their effective presence within photographs, assessed by the trained observers.We first checked the agreement between the trained observers for age classes and species and found a good agreement between them (Fleiss’ κ > 0.61 for juveniles of less than one and 12 month(s) old), suggesting that morphological criteria can be used successfully to determine age. The relationship between the proportion of volunteers detecting juveniles less than a month old and their actual presence plateaued at 0.45 for Grant’s gazelle and reached 0.70 for topi and 0.56 for kongoni. The same relationships were however much stronger for juveniles younger than 12 months, to the point that their presence was perfectly detected by volunteers for topi and kongoni.Volunteers’ classification allows a rough, moderately accurate, but quick, sorting of photograph sequences with/without juveniles. Obtaining accurate data however appears more difficult. We discuss the limitations of using citizen science camera traps data to study breeding phenology, and the options to improve the detection of juveniles, such as the addition of aging criteria on the online citizen science platforms, or the use of machine learning.

scholarly journals Real-time alerts from AI-enabled camera traps using the Iridium satellite network: a case-study in Gabon, Central Africa

2021 ◽  
Author(s):  
Robin C Whytock ◽  
Thijs Suijten ◽  
Tim van Deursen ◽  
Jędrzej Świeżewski ◽  
Hervé Mermiaghe ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Central Africa ◽  
Camera Trap ◽  
Lessons Learned ◽  
Camera Traps ◽  
Satellite Network ◽  
Consecutive Series ◽  
Time Data ◽  
Ecosystem Monitoring

Efforts to preserve, protect, and restore ecosystems are hindered by long delays between data collection and analysis. Threats to ecosystems can go undetected for years or decades as a result. Real-time data can help solve this issue but significant technical barriers exist. For example, automated camera traps are widely used for ecosystem monitoring but it is challenging to transmit images for real-time analysis where there is no reliable cellular or WiFi connectivity. Here, we present our design for a camera trap with integrated artificial intelligence that can send real-time information from anywhere in the world to end-users. We modified an off-the-shelf camera trap (Bushnell™) and customised existing open-source hardware to rapidly create a 'smart' camera trap system. Images captured by the camera trap are instantly labelled by an artificial intelligence model and an 'alert' containing the image label and other metadata is then delivered to the end-user within minutes over the Iridium satellite network. We present results from testing in the Netherlands, Europe, and from a pilot test in a closed-canopy forest in Gabon, Central Africa. Results show the system can operate for a minimum of three months without intervention when capturing a median of 17.23 images per day. The median time-difference between image capture and receiving an alert was 7.35 minutes. We show that simple approaches such as excluding 'uncertain' labels and labelling consecutive series of images with the most frequent class (vote counting) can be used to improve accuracy and interpretation of alerts. We anticipate significant developments in this field over the next five years and hope that the solutions presented here, and the lessons learned, can be used to inform future advances. New artificial intelligence models and the addition of other sensors such as microphones will expand the system's potential for other, real-time use cases. Potential applications include, but are not limited to, wildlife tourism, real-time biodiversity monitoring, wild resource management and detecting illegal human activities in protected areas.

Can camera trapping be used to accurately survey and monitor the Hastings River mouse (Pseudomys oralis)?

2016 ◽  
Vol 38 (1) ◽  
pp. 44 ◽  
Author(s):  
Paul D. Meek ◽  
Karl Vernes
Keyword(s):  
Small Mammals ◽  
Small Mammal ◽  
Survey Methods ◽  
Camera Trap ◽  
Camera Traps ◽  
Camera Trapping ◽  
Common Element ◽  
Computer Assisted ◽  
Survey Tool ◽  
Wide Range

Camera trapping is increasingly recognised as a survey tool akin to conventional small mammal survey methods such as Elliott trapping. While there are many cost and resource advantages of using camera traps, their adoption should not compromise scientific rigour. Rodents are a common element of most small mammal surveys. In 2010 we deployed camera traps to measure whether the endangered Hastings River mouse (Pseudomys oralis) could be detected and identified with an acceptable level of precision by camera traps when similar-looking sympatric small mammals were present. A comparison of three camera trap models revealed that camera traps can detect a wide range of small mammals, although white flash colour photography was necessary to capture characteristic features of morphology. However, the accurate identification of some small mammals, including P. oralis, was problematic; we conclude therefore that camera traps alone are not appropriate for P. oralis surveys, even though they might at times successfully detect them. We discuss the need for refinement of the methodology, further testing of camera trap technology, and the development of computer-assisted techniques to overcome problems associated with accurate species identification.

The history of wildlife camera trapping as a survey tool in Australia

2015 ◽  
Vol 37 (1) ◽  
pp. 1 ◽  
Author(s):  
Paul D. Meek ◽  
Guy-Anthony Ballard ◽  
Karl Vernes ◽  
Peter J. S. Fleming
Keyword(s):  
Quantitative Research ◽  
Reaction Times ◽  
Historical Review ◽  
Camera Trap ◽  
Recent Change ◽  
Camera Traps ◽  
Camera Trapping ◽  
Survey Tool ◽  
Trapping Methods ◽  
Effective Models

This paper provides an historical review of the technological evolution of camera trapping as a zoological survey tool in Australia. Camera trapping in Australia began in the 1950s when purpose-built remotely placed cameras were used in attempts to rediscover the thylacine (Thylacinus cynocephalus). However, camera traps did not appear in Australian research papers and Australasian conference proceedings until 1989–91, and usage became common only after 2008, with an exponential increase in usage since 2010. Initially, Australian publications under-reported camera trapping methods, often failing to provide fundamental details about deployment and use. However, rigour in reporting of key methods has increased during the recent widespread adoption of camera trapping. Our analysis also reveals a change in camera trap use in Australia, from simple presence–absence studies, to more theoretical and experimental approaches related to population ecology, behavioural ecology, conservation biology and wildlife management. Practitioners require further research to refine and standardise camera trap methods to ensure that unbiased and scientifically rigorous data are obtained from quantitative research. The recent change in emphasis of camera trapping research use is reflected in the decreasing range of camera trap models being used in Australian research. Practitioners are moving away from less effective models that have slow reaction times between detection and image capture, and inherent bias in detectability of fauna, to more expensive brands that offer faster speeds, greater functionality and more reliability.

scholarly journals Detection and Counting of Animals in Camera-Trap Images using Faster R-Cnn and Data Augmentation Techniques

2020 ◽  
Vol 9 (5) ◽  
pp. 931-943
Keyword(s):  
Data Augmentation ◽  
Camera Trap ◽  
Camera Traps ◽  
Counting Task ◽  
Original Dataset ◽  
Detection And Counting ◽  
Augmentation Techniques ◽  
The One ◽  
Future Work

Camera traps are used to recover images of animals in their habitats to help in the conservation of fauna. Millions of images are captured by camera traps and extracting information from these data delays and consumes enough resources so sometimes millions of images cannot be used due to lack of resources. That is why researchers have proposed solution approaches using Convolutional Neural Networks (CNNs) and object detection models to be able to automate the retrieval of information from these images. We used Faster R-CNN and data augmentation techniques on Gold Standard Snapshot Serengeti Dataset to detect animals in images and count them. The performances of the two models (the one trained on the original dataset and the one trained on the augmented dataset) were compared to show the importance of having more data for this task. Using the augmented dataset, we trained our model which reached an accuracy of 98.26% for classification of the proposed regions, an accuracy of 79.55% for counting the species present on the images and a mAP of 95.3%. For future work, the model can be trained to recognize the actions and characteristics of animals and tuned to be more efficient for counting task.

scholarly journals The Role of Citizen Science and Deep Learning in Camera Trapping

2021 ◽  
Vol 13 (18) ◽  
pp. 10287
Author(s):  
Matyáš Adam ◽  
Pavel Tomášek ◽  
Jiří Lehejček ◽  
Jakub Trojan ◽  
Tomáš Jůnek
Keyword(s):  
Machine Learning ◽  
Citizen Science ◽  
Camera Trap ◽  
Camera Traps ◽  
Public Authorities ◽  
Web Based ◽  
Conservation Policies ◽  
Terrestrial Vertebrate ◽  
New Perspective

Camera traps are increasingly one of the fundamental pillars of environmental monitoring and management. Even outside the scientific community, thousands of camera traps in the hands of citizens may offer valuable data on terrestrial vertebrate fauna, bycatch data in particular, when guided according to already employed standards. This provides a promising setting for Citizen Science initiatives. Here, we suggest a possible pathway for isolated observations to be aggregated into a single database that respects the existing standards (with a proposed extension). Our approach aims to show a new perspective and to update the recent progress in engaging the enthusiasm of citizen scientists and in including machine learning processes into image classification in camera trap research. This approach (combining machine learning and the input from citizen scientists) may significantly assist in streamlining the processing of camera trap data while simultaneously raising public environmental awareness. We have thus developed a conceptual framework and analytical concept for a web-based camera trap database, incorporating the above-mentioned aspects that respect a combination of the roles of experts’ and citizens’ evaluations, the way of training a neural network and adding a taxon complexity index. This initiative could well serve scientists and the general public, as well as assisting public authorities to efficiently set spatially and temporarily well-targeted conservation policies.

scholarly journals Applying camera traps to detect and monitor introduced mammals on oceanic islands

Oryx ◽  
2020 ◽  
pp. 1-8
Author(s):  
Lucas Lamelas-López ◽  
Iván Salgado
Keyword(s):  
Introduced Species ◽  
Large Scale ◽  
Camera Trap ◽  
Oceanic Islands ◽  
Camera Traps ◽  
Camera Trapping ◽  
Sampling Effort ◽  
Terrestrial Mammals ◽  
Management Actions ◽  
Survey Technique

Abstract The introduction of mammal predators has been a major cause of species extinctions on oceanic islands. Eradication is only possible or cost-effective at early stages of invasion, before introduced species become abundant and widespread. Although prevention, early detection and rapid response are the best management strategies, most oceanic islands lack systems for detecting, responding to and monitoring introduced species. Wildlife managers require reliable information on introduced species to guide, assess and adjust management actions. Thus, a large-scale and long-term monitoring programme is needed to evaluate the management of introduced species and the protection of native wildlife. Here, we evaluate camera trapping as a survey technique for detecting and monitoring introduced small and medium-sized terrestrial mammals on an oceanic island, Terceira (Azores). Producing an inventory of introduced mammals on this island required a sampling effort of 465 camera-trap days and cost EUR 2,133. We estimated abundance and population trends by using photographic capture rates as a population index. We also used presence/absence data from camera-trap surveys to calculate detection probability, estimated occupancy rate and the sampling effort needed to determine species absence. Although camera trapping requires large initial funding, this is offset by the relatively low effort for fieldwork. Our findings demonstrate that camera trapping is an efficient survey technique for detecting and monitoring introduced species on oceanic islands. We conclude by proposing guidelines for designing monitoring programmes for introduced species.

scholarly journals Improving Terrestrial Squamate Surveys with Camera-Trap Programming and Hardware Modifications

Animals ◽  
2019 ◽  
Vol 9 (6) ◽  
pp. 388 ◽  
Author(s):  
D. J. Welbourne ◽  
A. W. Claridge ◽  
D. J. Paull ◽  
F. Ford
Keyword(s):  
Focal Length ◽  
Time Lapse ◽  
Camera Trap ◽  
Camera Traps ◽  
Camera Trapping ◽  
Infrared Sensor ◽  
Vertebrate Fauna ◽  
The World ◽  
Lens Focal Length

Camera-traps are used widely around the world to census a range of vertebrate fauna, particularly mammals but also other groups including birds, as well as snakes and lizards (squamates). In an attempt to improve the reliability of camera-traps for censusing squamates, we examined whether programming options involving time lapse capture of images increased detections. This was compared to detections by camera-traps set to trigger by the standard passive infrared sensor setting (PIR), and camera-traps set to take images using time lapse in combination with PIR. We also examined the effect of camera trap focal length on the ability to tell different species of small squamate apart. In a series of side-by-side field comparisons, camera-traps programmed to take images at standard intervals, as well as through routine triggering of the PIR, captured more images of squamates than camera-traps using the PIR sensor setting alone or time lapse alone. Similarly, camera traps with their lens focal length set at closer distances improved our ability to discriminate species of small squamates. With these minor alterations to camera-trap programming and hardware, the quantity and quality of squamate detections was markedly better. These gains provide a platform for exploring other aspects of camera-trapping for squamates that might to lead to even greater survey advances, bridging the gap in knowledge of this otherwise poorly known faunal group.

scholarly journals A picture is worth a thousand words: the application of camera trapping to the study of birds

2008 ◽  
Vol 18 (S1) ◽  
pp. S144-S162 ◽  
Author(s):  
Timothy G. O'Brien ◽  
Margaret F. Kinnaird
Keyword(s):  
Finite Mixture Models ◽  
Camera Trap ◽  
Camera Traps ◽  
Camera Trapping ◽  
Individual Identification ◽  
Nest Defence ◽  
Primary Forest ◽  
Sampling Effort ◽  
Observation Process ◽  
Capture Recapture

AbstractThis study reviews the use of remotely triggered still cameras, known as camera traps, in bird research and suggests new methods useful for analyzing camera trap data. Camera trapping may be most appropriate for large, ground-dwelling birds, such as cracids and pheasants. Recent applications include documentation of occurrence of rare species and new species records, nest predation studies and behavioural studies including nest defence, frugivory, seed dispersal, and activity budgets. If bird postures are analyzed, it may be possible to develop behavioural time budgets. If birds are marked or individually identifiable, abundance may be estimated through capture-recapture methods typically used for mammals. We discourage use of relative abundance indices based on trapping effort because of the difficulty of standardizing surveys over time and space. Using the Great Argus Pheasant Argus argusianus, a cryptic, terrestrial, forest bird as an example, we illustrate applications of occupancy analysis to estimate proportion of occupied habitat and finite mixture models to estimate abundance when individual identification is not possible. These analyses are useful because they incorporate detection probabilities < 1 and covariates that affect the sample site or the observation process. Results are from camera trap surveys in the 3,568 km2 Bukit Barisan Selatan National Park, Indonesia. We confirmed that Great Argus Pheasants prefer primary forest below 500 m. We also find a decline in occupancy (6–8% yr−1). Point estimates of abundance peak in 2000, followed by a sharp decline. We discuss the effects of rarity, detection probability and sampling effort on accuracy and precision of estimates.

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