BearCam: automated wildlife monitoring at the arctic circle

Wawerla, Jens; Marshall, Shelley; Mori, Greg; Rothley, Kristina D.; Sabzmeydani, Payam

doi:10.1007/s00138-008-0128-0

Cited by 26 publications

(20 citation statements)

References 30 publications

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“…In previous studies on 3D location determination using a nonmetric camera, Rieke-Zapp and Nearing [40] and D'Amelio and Brutto [41] reported that RMS errors within 0.2 pixels appeared in the calibration of high-resolution cameras. Other studies have reported a range of RMS errors within a half 516 S. Lee et al Holman and Stanley [10] Wawerla et al [34] Newbery and Southwell [12] Bater et al [14,35] Monitoring system System name Holman and Stanley [10] Wawerla et al [34] Newbery and Southwell [12] Bater et al [14,35] Image capture pixel. [43,44] Chandler et al [45] observed that RMS errors within 0.3$0.4 pixel appeared in the calibration of a low-priced camera.…”

Section: Camera Calibrationmentioning

confidence: 96%

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Assessment of Smartphone-Based Technology for Remote Environmental Monitoring and Its Development

Lee

Jin

Bae

et al. 2012

Instrumentation Science & Technology

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& Many countries are increasing their research on monitoring technology to identify and systematically manage various domestic changes. In particular, the need for remote monitoring is increasing in response to climatic disasters, such as flooding, storms, and rising tides caused by global warming. We developed a smartphone-based environmental monitoring system that enables remote monitoring in any place and at any time. The overall system is composed of a 24-hour smartphonebased imaging system, a monitoring information management system to receive the monitoring information, and stereo image rectification software that provides lens distortion correction, geometric correction, and stereo matching of the monitoring images. The system was developed using the Samsung Galaxy S with the Android OS, as well as open source-based software and other hardware. It is easy to install, control remotely, and monitor the status of imaging devices. We assessed the accuracy of the micro-electro-mechanical system (MEMS) sensors of the smartphone to evaluate the applicability of our environmental monitoring system. The assessment was conducted via survey using metric cameras, a global positioning system receiver, a three-dimensional laser scanner and total station, geometric correction, and digital elevation models generated with camera internal elements, external elements, and ground control points. We demonstrated the effectiveness of the system, and showed that the accuracy of the MEMS sensor and camera calibration have a significant effect on image analysis.

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Section: Camera Calibrationmentioning

confidence: 96%

“…In this section, the proposed system is compared to several previously proposed systems, hereafter referred to as case A, [10] case B, [34] case C, [12] and case D, [14,35] and then evaluated.…”

Section: Smartphone-based Environmental Monitoring Systemmentioning

confidence: 99%

Assessment of Smartphone-Based Technology for Remote Environmental Monitoring and Its Development

Lee

Jin

Bae

et al. 2012

Instrumentation Science & Technology

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“…Face detection has also been used to classify species of African great apes using footage taken from video traps [35]. In settings most similar to this work, BearCam has been used to detect bears in the arctic circle during four hour daytime periods [36] by taking low level features such as image gradients and background differences and combining them into a mid-level motion shapelet [37] using AdaBoost [38].…”

Section: A Detecting Animals In Videomentioning

confidence: 99%

Wildlife@Home: Combining Crowd Sourcing and Volunteer Computing to Analyze Avian Nesting Video

Desell

Bergman

Goehner

et al. 2013

2013 IEEE 9th International Conference on E-Science

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New camera technology is allowing avian ecologists to perform detailed studies of avian behavior, nesting strategies and predation in areas where it was previously impossible to gather data. Unfortunately, studies have shown mechanical triggers and a variety of sensors to be inadequate in capturing footage of small predators (e.g., snakes, rodents) or events in dense vegetation. Because of this, continuous camera recording is currently the most robust solution for avian monitoring, especially in ground nesting species. However, continuous video footage results in a data deluge, as monitoring enough nests to make biologically significant inferences results in massive amounts of data which is unclassifiable by humans alone. In the summer of 2012, Dr. Ellis-Felege gathered video footage from 63 sharp-tailed grouse (Tympanuchus phasianellus) nests, as well as preliminary interior least tern (Sternula antillarum) and piping plover (Charadrius melodus) nests, resulting in over 20,000 hours of video footage. In order to effectively analyze this video, a project combining both crowd sourcing and volunteer computing was developed, where volunteers can stream nesting video and report their observations, as well as have their computers download video for analysis by computer vision techniques. This provides a robust way to analyze the video, as user observations are validated by multiple views as well as the results of the computer vision techniques. This work provides initial results analyzing the effectiveness of the crowd sourced observations and computer vision techniques.

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“…Wawerla et al [17] describe a system to monitor the behavior of grizzly bears at the arctic circle with camera traps. They use motion http://jivp.eurasipjournals.com/content/2013/1/49 shapelet features and AdaBoost to detect bears in video footage.…”

Section: Visual Detectionmentioning

confidence: 99%

An automated chimpanzee identification system using face detection and recognition

Loos

Ernst

2013

J Image Video Proc

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Due to the ongoing biodiversity crisis, many species including great apes like chimpanzees are on the brink of extinction. Consequently, there is an urgent need to protect the remaining populations of threatened species. To overcome the catastrophic decline of biodiversity, biologists and gamekeepers recently started to use remote cameras and recording devices for wildlife monitoring in order to estimate the size of remaining populations. However, the manual analysis of the resulting image and video material is extremely tedious, time consuming, and cost intensive. To overcome the burden of time-consuming routine work, we have recently started to develop computer vision algorithms for automated chimpanzee detection and identification of individuals. Based on the assumption that humans and great apes share similar properties of the face, we proposed to adapt and extend face detection and recognition algorithms, originally developed to recognize humans, for chimpanzee identification. In this paper we do not only summarize our earlier work in the field, we also extend our previous approaches towards a more robust system which is less prone to difficult lighting situations, various poses, and expressions as well as partial occlusion by branches, leafs, or other individuals. To overcome the limitations of our previous work, we combine holistic global features and locally extracted descriptors using a decision fusion scheme. We present an automated framework for photo identification of chimpanzees including face detection, face alignment, and face recognition. We thoroughly evaluate our proposed algorithms on two datasets of captive and free-living chimpanzee individuals which were annotated by experts. In three experiments we show that the presented framework outperforms previous approaches in the field of great ape identification and achieves promising results. Therefore, our system can be used by biologists, researchers, and gamekeepers to estimate population sizes faster and more precisely than the current frameworks. Thus, the proposed framework for chimpanzee identification has the potential to open up new venues in efficient wildlife monitoring and can help researches to develop innovative protection schemes in the future.

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BearCam: automated wildlife monitoring at the arctic circle

Cited by 26 publications

References 30 publications

Assessment of Smartphone-Based Technology for Remote Environmental Monitoring and Its Development

Assessment of Smartphone-Based Technology for Remote Environmental Monitoring and Its Development

Wildlife@Home: Combining Crowd Sourcing and Volunteer Computing to Analyze Avian Nesting Video

An automated chimpanzee identification system using face detection and recognition

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