Wild Animal Detection from Highly Cluttered Forest Images Using Deep Residual Networks

2018 ◽  
pp. 230-238
Author(s):  
Anamika Dhillon ◽  
Gyanendra K. Verma
Keyword(s):  
Wild Animal ◽  
Animal Detection

Battery-Powered Wild Animal Detection Nodes with Deep Learning

2020 ◽  
Vol E103.B (12) ◽  
pp. 1394-1402
Author(s):  
Hiroshi SAITO ◽  
Tatsuki OTAKE ◽  
Hayato KATO ◽  
Masayuki TOKUTAKE ◽  
Shogo SEMBA ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wild Animal ◽  
Animal Detection

scholarly journals A tribrid Paradigm against Inundation wild animal attack and fire disasters

2019 ◽  
Vol 9 (2S3) ◽  
pp. 454-457
Keyword(s):  
Disaster Management ◽  
Fire Detection ◽  
Wild Animal ◽  
Red Cross ◽  
Remedial Action ◽  
The Common ◽  
Government Records ◽  
Animal Detection ◽  
Detection Unit ◽  
Animal Attack

In recent years the whole world witnessed several natural and manmade disasters. 2015 Earthquake in Nepal and India with 7.8 magnitudes which killed 9000 people and injured 22000 as per Government Records. 2018 Flood in North Korea left 10,700 people as per the report of International Federation of Red Cross (IFRC) and Wikipedia. Floods in India 2018, killed more than 300 in the state of Kerala. 2016, Forest fire in Uttarakhand, India burnt 10,000 acres of Forest area. In between April 2014 – May 2017, 1,144 people killed by wild animals as per the report of Indian Environment Ministry (IEM). All the reports stated above represents indications that regardless of several advancements and technical skill development to disaster management is not considered efficiently worldwide. There are plenty of disasters which could be taken care of much efficiently and wisely. If we see the disasters like flood, fire and animal attack we can easily notice that they are manageable to much extent with expert technical advancements. Our paper is about the disaster management of the tribrid series (Flood, Fire, Wild Animal Attack) with the eminent usage of technology. We create a flood sensing unit, a fire detection unit, and a wild animal detection unit with the help of sensors and we create the alert and remedial action unit to protect the common men from sub disasters. We use Embedded and IOT technologies together to provide worldwide coverage and accurate detection of the calamities.

Wild Animal Detection from Highly Cluttered Images Using Deep Convolutional Neural Network

2018 ◽  
Vol 17 (04) ◽  
pp. 1850021
Author(s):  
Gyanendra K. Verma ◽  
Pragya Gupta
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Camera Trap ◽  
Wild Animal ◽  
Deep Convolutional Neural Network ◽  
Multimedia Data ◽  
Machine Learning Techniques ◽  
Wild Animals ◽  
Animal Detection

Monitoring wild animals became easy due to camera trap network, a technique to explore wildlife using automatically triggered camera on the presence of wild animal and yields a large volume of multimedia data. Wild animal detection is a dynamic research field since the last several decades. In this paper, we propose a wild animal detection system to monitor wildlife and detect wild animals from highly cluttered natural images. The data acquired from the camera-trap network comprises of scenes that are highly cluttered that poses a challenge for detection of wild animals bringing about low recognition rates and high false discovery rates. To deal with the issue, we have utilized a camera trap database that provides candidate regions utilizing multilevel graph cut in the spatiotemporal area. The regions are utilized to make a validation stage that recognizes whether animals are present or not in a scene. These features from cluttered images are extracted using Deep Convolutional Neural Network (CNN). We have implemented the system using two prominent CNN models namely VGGNet and ResNet, on standard camera trap database. Finally, the CNN features fed to some of the best in class machine learning techniques for classification. Our outcomes demonstrate that our proposed system is superior compared to existing systems reported in the literature.

SYSTEMS OF ANIMAL DETECTION ON HIGHWAYS. EXPERIENCE OF USA AND EUROPEAN COUNTRIES

2021 ◽  
Vol 2021 (24) ◽  
pp. 177-182
Author(s):  
Nataliia Kharytonova ◽  
◽  
Tetiana Lozova ◽  
Keyword(s):  
Western Europe ◽  
Natural Habitat ◽  
Wild Animal ◽  
Wild Animals ◽  
Road Accidents ◽  
Problem Statement ◽  
Detection Systems ◽  
Foreign Countries ◽  
History Of ◽  
Animal Detection

Introduction. The experience of the countries of Western Europe and USA was analyzed, the purposes and methods of implementation of wild animals detection systems on highways are developed. Problem statement. The reduction of the natural habitat of wild animals leads to an increase in the number of road accidents. Animal detection systems are aimed at reducing the frequency of collisions between wild animals and vehicles. Purpose. The purpose of the study is to analyze the literature on the effectiveness and evaluation of the implementation of wild animal detection systems; to elaborate sources that describe the history of implementation and experience of foreign countries. Materials and methods. Analysis of foreign sources on the implementation of wild animal detection systems. Results. The analysis of foreign information sources on the implementation of wild animal detection systems was carried out. Conclusions. The experience of the countries of Western Europe and North America is described, the efficiency of implementation of wild animal detection systems is analyzed.

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