Motion vectors and deep neural networks for video camera traps

Commercial camera traps are usually triggered by a Passive Infra-Red (PIR) motion sensor necessitating a delay between triggering and the image being captured. This often seriously limits the ability to record images of small and fast moving animals. It also results in many “empty” images, e.g., owing to moving foliage against a background of different temperature. In this paper we detail a new triggering mechanism based solely on the camera sensor. This is intended for use by citizen scientists and for deployment on an affordable, compact, low-power Raspberry Pi computer (RPi). Our system introduces a video frame filtering pipeline consisting of movement and image-based processing. This makes use of Machine Learning (ML) feasible on a live camera stream on an RPi. We describe our free and open-source software implementation of the system; introduce a suitable ecology efficiency measure that mediates between specificity and recall; provide ground-truth for a video clip collection from camera traps; and evaluate the effectiveness of our system thoroughly. Overall, our video camera trap turns out to be robust and effective.     

Design patterns for wildlife‐related camera trap image analysis

This paper describes and explains design patterns for software that supports how analysts can efficiently inspect and classify camera trap images for wildlife‐related ecological attributes. Broadly speaking, a design pattern identifies a commonly occurring problem and a general reusable design approach to solve that problem. A developer can then use that design approach to create a specific software solution appropriate to the particular situation under consideration. In particular, design patterns for camera trap image analysis by wildlife biologists address solutions to commonly occurring problems they face while inspecting a large number of images and entering ecological data describing image attributes. We developed design patterns for image classification based on our understanding of biologists' needs that we acquired over 8 years during development and application of the freely available Timelapse image analysis system. For each design pattern presented, we describe the problem, a design approach that solves that problem, and a concrete example of how Timelapse addresses the design pattern. Our design patterns offer both general and specific solutions related to: maintaining data consistency, efficiencies in image inspection, methods for navigating between images, efficiencies in data entry including highly repetitious data entry, and sorting and filtering image into sequences, episodes, and subsets. These design patterns can inform the design of other camera trap systems and can help biologists assess how competing software products address their project‐specific needs along with determining an efficient workflow.     

Bring your own camera to the trap: An inexpensive,versatile, and portable triggering system tested on wild hummingbirds

The study of animals in the wild offers opportunities to collect relevant information on their natural behavior and abilities to perform ecologically relevant tasks. However, it also poses challenges such as accounting for observer effects, human sensory limitations, and the time intensiveness of this type of research. To meet these challenges, field biologists have deployed camera traps to remotely record animal behavior in the wild. Despite their ubiquity in research, many commercial camera traps have limitations, and the species and behavior of interest may present unique challenges. For example, no camera traps support high‐speed video recording. We present a new and inexpensive camera trap system that increases versatility by separating the camera from the triggering mechanism. Our system design can pair with virtually any camera and allows for independent positioning of a variety of sensors, all while being low‐cost, lightweight, weatherproof, and energy efficient. By using our specialized trigger and customized sensor configurations, many limitations of commercial camera traps can be overcome. We use this system to study hummingbird feeding behavior using high‐speed video cameras to capture fast movements and multiple sensors placed away from the camera to detect small body sizes. While designed for hummingbirds, our application can be extended to any system where specialized camera or sensor features are required, or commercial camera traps are cost‐prohibitive, allowing camera trap use in more research avenues and by more researchers.     

High‐resolution photo‐mosaic time‐series imagery for monitoring human use of an artificial reef

Successful marine management relies on understanding patterns of human use. However, obtaining data can be difficult and expensive given the widespread and variable nature of activities conducted. Remote camera systems are increasingly used to overcome cost limitations of conventional labour‐intensive methods. Still, most systems face trade‐offs between the spatial extent and resolution over which data are obtained, limiting their application. We trialed a novel methodology, CSIRO Ruggedized Autonomous Gigapixel System (CRAGS), for time series of high‐resolution photo‐mosaic (HRPM) imagery to estimate fine‐scale metrics of human activity at an artificial reef located 1.3 km from shore. We compared estimates obtained using the novel system to those produced with a web camera that concurrently monitored the site. We evaluated the effect of day type (weekday/weekend) and time of day on each of the systems and compared to estimates obtained from binocular observations. In general, both systems delivered similar estimates for the number of boats observed and to those obtained by binocular counts; these results were also unaffected by the type of day (weekend vs. weekday). CRAGS was able to determine additional information about the user type and party size that was not possible with the lower resolution webcam system. However, there was an effect of time of day as CRAGS suffered from poor image quality in early morning conditions as a result of fixed camera settings. Our field study provides proof of concept of use of this new cost‐effective monitoring tool for the remote collection of high‐resolution large‐extent data on patterns of human use at high temporal frequency.     

How to build and install your own CaptuRING

CaptuRING is a reliable and affordable tool to transform tree-ring samples into digital images combining open source software and do-it-yourself philosophies. A Raspberry Pi runs the system through an Arduino board that controls the wood sample movement across a linear screw at the time that a digital camera takes sequential high resolution (>4500 dpi) images from a wood sample. Here, we present three video tutorials, with English and Spanish subtitles, to construct and install CaptuRING (github.com/CambiumRG/CaptuRING) from scratch. First video tutorial explains the necessary components and how to assemble them to construct the CaptuRING platform, second tutorial covers Arduino board and Raspberry Pi connections, and the third tutorial is devoted to hardware configuration, software installation and CaptuRING use.     

An inexpensive camera system for monitoring cavity nests

ABSTRACT A variety of pole‐mounted cameras have been developed for monitoring nest cavities. However, currently available camera systems may either be prohibitively expensive or difficult to assemble. I developed an inexpensive (<$500 US) and easily assembled camera system that allows researchers to monitor cavity nests from the ground. The system consists of a small camera, a cable connecting the camera to a ground‐level power source and laptop computer, and a flexible neck connecting the camera to a telescoping pole. During a study of Red‐headed Woodpeckers (Melanerpes erythrocephalus), I used this camera to inspect 16 nests and found that the images were clear and allowed accurate counts of eggs and nestlings. This camera system uses standard, off‐the‐shelf components, and can easily be altered. The design is not appropriate for humid or dense‐canopy environments because of the inclusion of a laptop and its wired design. However, this design makes the system inexpensive and allows researchers to save, edit, and view nest inspection recordings.     

A wireless video camera for viewing tree cavities

ABSTRACT Investigators have used a variety of methods to inspect nest cavities, including wireless battery‐powered video cameras mounted on telescoping poles. Using such a monitoring system to inspect cavities located well above ground can be difficult because the weight of the camera can cause flexing of the telescoping pole, making it difficult to insert the camera into cavity entrances. We constructed a system made from commercially available products that transmits wireless video images from nest cavities, and is both lightweight (198 g) and relatively inexpensive (about $520 US). During a study of Pileated Woodpeckers (Dryocopus pileatus), we inspected more than 100 cavities using our monitoring system and found that images were clear enough to allow us to count eggs and nestlings, and determine the sex of adults and nestlings. Because of its light weight, our wireless camera system allows quick inspection of cavities (typically less than 2 min). Although we used our cavity‐monitoring system to inspect cavities used by Pileated Woodpecker, we believe that the diameter of the camera could be reduced from 5.6 cm to 4.7 cm to allow inspection of cavities with smaller entrances.     

Comparing motion capture cameras versus human observer monitoring of mammal movement through fence gaps: a case study from Kenya

Monitoring the movement and distribution of wildlife is a critical tool of an adaptive management framework for wildlife conservation. We installed motion‐triggered cameras to capture the movement of mammals through two purpose‐built migration gaps in an otherwise fenced conservancy in northern Kenya. We compared the results to data gathered over the same time period (1 Jan 2011–31 Dec 2012) by the human observers monitoring mammal tracks left at the same fence gaps in a sandy loam detection strip. The camera traps detected more crossing events, more species and more individuals of each species per crossing event than did the human track observers. We tested for volume detection differences between methods for the five most common species crossing each gap and found that all detection rates were heavily weighted towards the camera‐trap method. We review some of the discrepancies between the methods and conclude that although the camera traps record more data, the management of that data can be time‐consuming and ill‐suited to some time‐sensitive decision‐making. We also discuss the importance of daily track monitoring for adaptive management conservation and community security.     

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

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.     

A comparison of camera trap and permanent recording video camera efficiency in wildlife underpasses

In the current context of biodiversity loss through habitat fragmentation, the effectiveness of wildlife crossings, installed at great expense as compensatory measures, is of vital importance for ecological and socio‐economic actors. The evaluation of these structures is directly impacted by the efficiency of monitoring tools (camera traps…), which are used to assess the effectiveness of these crossings by observing the animals that use them. The aim of this study was to quantify the efficiency of camera traps in a wildlife crossing evaluation. Six permanent recording video systems sharing the same field of view as six Reconyx HC600 camera traps installed in three wildlife underpasses were used to assess the exact proportion of missed events (event being the presence of an animal within the field of view), and the error rate concerning underpass crossing behavior (defined as either Entry or Refusal). A sequence of photographs was triggered by either animals (true trigger) or artefacts (false trigger). We quantified the number of false triggers that had actually been caused by animals that were not visible on the images (“false” false triggers). Camera traps failed to record 43.6% of small mammal events (voles, mice, shrews, etc.) and 17% of medium‐sized mammal events. The type of crossing behavior (Entry or Refusal) was incorrectly assessed in 40.1% of events, with a higher error rate for entries than for refusals. Among the 3.8% of false triggers, 85% of them were “false” false triggers. This study indicates a global underestimation of the effectiveness of wildlife crossings for small mammals. Means to improve the efficiency are discussed.     

A single‐nucleotide polymorphism‐based approach for rapid and cost‐effective genetic wolf monitoring in Europe based on noninvasively collected samples

Noninvasive genetics based on microsatellite markers has become an indispensable tool for wildlife monitoring and conservation research over the past decades. However, microsatellites have several drawbacks, such as the lack of standardisation between laboratories and high error rates. Here, we propose an alternative single‐nucleotide polymorphism (SNP)‐based marker system for noninvasively collected samples, which promises to solve these problems. Using nanofluidic SNP genotyping technology (Fluidigm), we genotyped 158 wolf samples (tissue, scats, hairs, urine) for 192 SNP loci selected from the Affymetrix v2 Canine SNP Array. We carefully selected an optimised final set of 96 SNPs (and discarded the worse half), based on assay performance and reliability. We found rates of missing data in this SNP set of <10% and genotyping error of ~1%, which improves genotyping accuracy by nearly an order of magnitude when compared to published data for other marker types. Our approach provides a tool for rapid and cost‐effective genotyping of noninvasively collected wildlife samples. The ability to standardise genotype scoring combined with low error rates promises to constitute a major technological advancement and could establish SNPs as a standard marker for future wildlife monitoring.     

Telemetering sub aqua video photography to boat‐ and shore‐bases: a new approach

The use of video photography normally involves sub‐aqua recording via a camcorder device. The problem is that the scientist needs to be directly involved in the recording underwater or else viewing the results of the footage after the event. The system we have developed enables real time images to be relayed to a boat, satellite of shore‐based recorder. With a two‐way audio communication system, it is possible for the scientist/team to direct the camera work of the divers, remotely. The configuration of this system are discussed, and a wide range of scientific applications suggested.     

Wildlife surveillance using deep learning methods

相似文献   

An economical wireless cavity-nest viewer

ABSTRACT.   Inspection of cavity nests and nest boxes is often required during studies of cavity-nesting birds, and fiberscopes and pole-mounted video cameras are sometimes used for such inspection. However, the cost of these systems may be prohibitive for some potential users. We describe a user-built, wireless cavity viewer that can be used to access cavities as high as 15 m and with entrance diameters as small as 2.5 cm. System components include a wireless camera, boom with near-infrared lighting system, hand-held television, and telescoping pole. The unit can be assembled in about one day with commonly available tools, and the total cost was about $850 (US). Our cavity-viewing system was used to monitor the nests of Western Bluebirds ( Sialia mexicana ) and proved to be reliable and effective. During two field seasons, we attempted to check the status of nests 928 times and were able to determine the nesting stage 901 times (97%). For those willing to assemble their own units, our cavity viewer permits safe, direct examination of cavity nests or open nests for investigators with limited budgets.     

Animal Scanner: Software for classifying humans,animals, and empty frames in camera trap images

Camera traps are a popular tool to sample animal populations because they are noninvasive, detect a variety of species, and can record many thousands of animal detections per deployment. Cameras are typically set to take bursts of multiple photographs for each detection and are deployed in arrays of dozens or hundreds of sites, often resulting in millions of photographs per study. The task of converting photographs to animal detection records from such large image collections is daunting, and made worse by situations that generate copious empty pictures from false triggers (e.g., camera malfunction or moving vegetation) or pictures of humans. We developed computer vision algorithms to detect and classify moving objects to aid the first step of camera trap image filtering—separating the animal detections from the empty frames and pictures of humans. Our new work couples foreground object segmentation through background subtraction with deep learning classification to provide a fast and accurate scheme for human–animal detection. We provide these programs as both Matlab GUI and command prompt developed with C++. The software reads folders of camera trap images and outputs images annotated with bounding boxes around moving objects and a text file summary of results. This software maintains high accuracy while reducing the execution time by 14 times. It takes about 6 seconds to process a sequence of ten frames (on a 2.6 GHZ CPU computer). For those cameras with excessive empty frames due to camera malfunction or blowing vegetation automatically removes 54% of the false‐triggers sequences without influencing the human/animal sequences. We achieve 99.58% on image‐level empty versus object classification of Serengeti dataset. We offer the first computer vision tool for processing camera trap images providing substantial time savings for processing large image datasets, thus improving our ability to monitor wildlife across large scales with camera traps.     

Open questions and recent advances in the control of a multi‐host infectious disease: animal tuberculosis

相似文献   

Use of non-stop video surveillance to monitor breeding activity of primary cavity nesters in remote areas

I developed a video surveillance system to monitor breeding activity of primary cavity nesters in remote areas. A small video camera was installed inside a nesting cavity and I used wireless transmission of the video signal from the nest tree to a field station equipped with electricity to record behaviour. In a pilot study, I documented parental activities in a nest of the three-toed woodpecker (Picoides tridactylus). The female stayed with the chicks at night two times, whereas the male did this regularly. Adults did not always incubate or brood while on the nest but instead spent some time in motion. The described system is suitable for studying nesting behaviour of species living in remote areas with difficult access, because the proposed wireless transmission of the video signal minimizes manual operations at the nesting tree.     

Long‐term persistence of wildlife populations in a pastoral area

Facilitating coexistence between people and wildlife is a major conservation challenge in East Africa. Some conservation models aim to balance the needs of people and wildlife, but the effectiveness of these models is rarely assessed. Using a case‐study approach, we assessed the ecological performance of a pastoral area in northern Tanzania (Manyara Ranch) and established a long‐term wildlife population monitoring program (carried out intermittently from 2003 to 2008 and regularly from 2011 to 2019) embedded in a distance sampling framework. By comparing density estimates of the road transect‐based long‐term monitoring to estimates derived from systematically distributed transects, we found that the bias associated with nonrandom placement of transects was nonsignificant. Overall, cattle and sheep and goat reached the greatest densities and several wildlife species occurred at densities similar (zebra, wildebeest, waterbuck, Kirk's dik‐dik) or possibly even greater (giraffe, eland, lesser kudu, Grant's gazelle, Thomson's gazelle) than in adjacent national parks in the same ecosystem. Generalized linear mixed models suggested that most wildlife species (8 out of 14) reached greatest densities during the dry season, that wildlife population densities either remained constant or increased over the 17‐year period, and that herbivorous livestock species remained constant, while domestic dog population decreased over time. Cross‐species correlations did not provide evidence for interference competition between grazing or mixed livestock species and wildlife species but indicate possible negative relationships between domestic dog and warthog populations. Overall, wildlife and livestock populations in Manyara Ranch appear to coexist over the 17‐year span. Most likely, this is facilitated by existing connectivity to adjacent protected areas, effective anti‐poaching efforts, spatio‐temporal grazing restrictions, favorable environmental conditions of the ranch, and spatial heterogeneity of surface water and habitats. This long‐term case study illustrates the potential of rangelands to simultaneously support wildlife conservation and human livelihood goals if livestock grazing is restricted in space, time, and numbers.     

Software for minimalistic data management in large camera trap studies

The use of camera traps is now widespread and their importance in wildlife studies is well understood. Camera trap studies can produce millions of photographs and there is a need for a software to help manage photographs efficiently. In this paper, we describe a software system that was built to successfully manage a large behavioral camera trap study that produced more than a million photographs. We describe the software architecture and the design decisions that shaped the evolution of the program over the study's three year period. The software system has the ability to automatically extract metadata from images, and add customized metadata to the images in a standardized format. The software system can be installed as a standalone application on popular operating systems. It is minimalistic, scalable and extendable so that it can be used by small teams or individual researchers for a broad variety of camera trap studies.     

Integrated Life Cycle Assessment and Life Cycle Cost Model for Comparing Plug‐in versus Wireless Charging for an Electric Bus System

An integrated life cycle assessment and life cycle cost (LCC) model was developed to compare the life cycle performance of plug‐in charging versus wireless charging for an electric bus system. The model was based on a bus system simulation using existing transit bus routes in the Ann Arbor–Ypsilanti metro area in Michigan. The objective is to evaluate the LCCs for an all‐electric bus system utilizing either plug‐in or wireless charging and also compare these costs to both conventional pure diesel and hybrid bus systems. Despite a higher initial infrastructure investment for off‐board wireless chargers deployed across the service region, the wireless charging bus system has the lowest LCC of US$0.99 per bus‐kilometer among the four systems and has the potential to reduce use‐phase carbon emissions attributable to the lightweighting benefits of on‐board battery downsizing compared to plug‐in charging. Further uncertainty analysis and sensitivity analysis indicate that the unit price of battery pack and day or night electricity price are key parameters in differentiating the LCCs between plug‐in and wireless charging. Additionally, scenario analyses on battery recycling, carbon emission pricing, and discount rates were conducted to further analyze and compare their respective life cycle performance.