Where's The Bear?

Elias, Andy Rosales; Golubovic, Nevena; Krintz, Chandra; Wolski, Rich

doi:10.1145/3054977.3054986

Cited by 49 publications

(13 citation statements)

References 14 publications

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“…Minimizing human visitation also will reduce human scents and disturbances that could deter some species from visiting cameras. In the future, it may be possible to implement a machine learning model on a game camera (Elias et al, 2017) that automatically classifies images as empty or containing animals so that empty images are discarded immediately and not stored on the camera. This type of approach could dramatically reduce the frequency with which technicians need to visit cameras.…”

Section: Discussionmentioning

confidence: 99%

Machine learning to classify animal species in camera trap images: applications in ecology

Tabak

Sadegh

Wolfson

et al. 2018

Preprint

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All rights reserved. No reuse allowed without permission.(which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. from Canada. We also tested the ability of our model to distinguish empty images from those 56 with animals in another out-of-sample dataset from Tanzania, containing a faunal community 57 that was novel to the model. (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.The copyright holder for this preprint . http://dx.doi.org/10.1101/346809 doi: bioRxiv preprint first posted online Jun. 13, 2018; 3 4. The use of machine learning to rapidly and accurately classify wildlife in camera trap images 66 can facilitate non-invasive sampling designs in ecological studies by reducing the burden of 67 manually analyzing images. We present an R package making these methods accessible to 68 ecologists. We discuss the implications of this technology for ecology and considerations that 69 should be addressed in future implementations of these methods. 70

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Section: Discussionmentioning

confidence: 99%

Machine learning to classify animal species in camera trap images: applications in ecology

Tabak

Sadegh

Wolfson

et al. 2018

Preprint

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“…Ghayvat et al [18] proposed a scheme which develops a new protocol for address smart home for living. This protocol knows as wellness sensors network.…”

Section: Related Workmentioning

confidence: 99%

The Role of Machine Learning in Internet-of-Things (IoT) Research: A Review

Desai¹,

Jhaveri²

2018

IJCA

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Rapid developments in hardware and software based connected devices with communication technologies introduce Internet-of-things (IoT). In recent year, IoT gained enormous popularity and used vastly in a variety of applications. IoT represents the new forms of communication such as between human and things, and between two things. At present era IoT concepts are highly influence by creating a new dimension in the internet world. Intelligent processing and analysis of big data is the key to developing smart IoT applications. Such applications are logistic, transportation, agriculture, healthcare, and environment. Increasing the use of internet and external factor of environment are caused dynamic change in IoT system. The use of smart environments in the delivery of a pervasive care is the research topic that has witnessed increasing interest in recent years. These smart environments aim to deliver pervasive care through ubiquitous sensing by monitoring the occupant's activities. In order to provide smarter environment, their need to be implement IoT with machine learning. In recent year, machine learning technique have been used widely because of its technologies such that identification, extraction, classification, regression and forecasting. Machine learning exploring historical data from camera and sensors and perform techniques which improve the lifespan of network. In this paper, we build survey on existing research work carried out for various applications of machine learning to IoT. We summarize techniques and tools for IoT and also their benefits and limitations. Using a summarized data we also look for different future challenges. The aim of this paper is to survey different IoT technologies to assist the people to live in a smart environment.

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“…This gives advantage of scaling up for larger images [8] [9].Transmission of huge video data into the cloud has been performed using remote IOT server to progress the process smoothly [10]. An end-to-end IOT edge cloud system for image processing and analytics which enables the monitoring of wild life in remote locations automatically has been developed as shown in figure 5 [11]. The system is customized and uses camera pictures which contains images of bear, dear, etc., IOT system connects all the motion triggered cameras with the help of Wi-Fi through a distributed system.…”

Section: Image Processing Applicationsmentioning

confidence: 99%

Internet of things and its applications

Sai¹,

Kumar²

2018

IJET

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IOT is creating impeccable things by improving the performance of system in the field of communications in many technical applications in expedite manner and taken the system performance to next level. implementing IOT in today's world out do's the response time of the system to the normal system response in R&D applications. this paper depicts the challenges and the problems in various domains and gave the solution by imbibing IOT Technology by standardizing the system processes to meet the industrial as well as domestic needs.

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Where's The Bear?

Cited by 49 publications

References 14 publications

Machine learning to classify animal species in camera trap images: applications in ecology

Machine learning to classify animal species in camera trap images: applications in ecology

The Role of Machine Learning in Internet-of-Things (IoT) Research: A Review

Internet of things and its applications

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