A Review of Animal Intrusion Detection System

Jeevitha, S.; Kumar, V. Jaiganesh P. Vasantha

doi:10.17577/ijertv9is050351

Cited by 13 publications

(4 citation statements)

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“…Furthermore, the use of IoT and deep learning is proposed to detect animals and issue warnings to prevent crop destruction in agricultural areas. Limitations include the lack of an application, restricting result viewing to the sensor set screen, and the absence of presented accuracy evaluation results [30].…”

Section: Related Workmentioning

confidence: 99%

“…Notably, the utilization of deep learning, supported by comprehensive and highly accurate training data, achieves accuracy rates surpassing 90% [23], [25]. Nevertheless, relying solely on machine learning techniques lacks real-time surveillance and tracking capabilities, prompting efforts to address these limitations by advocating for the combined use of IoT and machine learning for animal detection and monitoring across diverse applications [27], [28], [29], [30], [31]. The strengths of the latter method include the ability to monitor animal movement and appearance data in real-time, providing highly accurate results and facilitating realtime notifications to users through various formats such as SMS and application alerts.…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Threat Prevention System for Mitigating Intrusions by Dogs in Livestock Farming using IoT and Machine Learning

Saeliw,

Hualkasin,

Puttinaovarat

2024

TEM Journal

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One of the challenges encountered by farmers engaged in livestock farming is the menace posed by stray or ownerless dogs, causing harm to the animals being raised on the farm. This not only adversely affects the health of the animals but also impacts the overall cost associated with their upbringing. Consequently, this research introduces the development of a sophisticated system aimed at preventing threats and intrusions by dogs that pose harm to farm animals. The system leverages Internet of Things (IoT) technology and employs Machine Learning algorithms, specifically Convolutional Neural Network, for real-time tracking and monitoring. The research findings reveal that the developed system demonstrates a high level of efficiency, swiftly and accurately classifying animals entering areas equipped with cameras, achieving an impressive accuracy rate of 92.54%. Furthermore, the system is equipped to promptly notify users and emit deterrent sounds to repel dogs entering the monitored area, enhancing its effectiveness in safeguarding livestock and optimizing farm management practices.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Real-Time Threat Prevention System for Mitigating Intrusions by Dogs in Livestock Farming using IoT and Machine Learning

Saeliw,

Hualkasin,

Puttinaovarat

2024

TEM Journal

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“…Deep Belief Networks (DBNs) are a distinctive class of generative models characterised by their multi-layered architecture, comprising stochastic, latent variables [45]. This unique structure enables DBNs to excel in unsupervised learning tasks, mainly feature learning and dimensionality reduction.…”

Section: Deep Learning Architectures For Animalmentioning

confidence: 99%

A Comprehensive Review of Deep Learning Approaches for Animal Detection on Video Data

Kumar,

Luo,

Shaukat

2023

IJACSA

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Integrating deep learning techniques into computer vision application has ushered in a new era of automated analysis and interpretation of visual data. In recent years, a surge of interest has been witnessed in applying these methodologies towards detecting animals in video streams, promising transformative impacts on diverse fields such as ecology and agriculture. This paper presents an extensive and meticulous review of the latest deep-learning approaches employed for animal detection in video data. This study looks closely at ways to detect animals in videos using deep learning. This study explores various Deep learning methods for detecting many animals in multiple environments. The analysis also pays close attention to preparing the data, picking out important features, and reusing what has been learned from one task to help with another. In addition to highlighting successful methodologies, this review addresses the challenges and limitations inherent in these approaches issues such as limited data availability and adapting to technological advancements present significant hurdles. Recognising and understanding these challenges is crucial in shaping the future focus of research endeavours. Thus, this comprehensive review is an indispensable tool for anyone keen on employing these potent computer methods for animal detection in videos. It takes the latest ideas and shows where study can explore further to improve them. Furthermore, this comprehensive review has demonstrated that a more sustainable and balanced relationship between humans and animals can be achieved by harnessing the power of deep learning in animal detection. This research contributes to computer vision and holds immense promise in safeguarding biodiversity and promoting responsible land use practices, especially within agricultural domains. The insights from this study propel us towards a future where advanced technology and ecological harmony go hand in hand, ultimately benefiting both humans and the animal kingdom. The survey aims to provide a comprehensive overview of the cutting-edge developments in applying deep learning models for animal detection through cameras by elucidating the significance of these techniques in advancing the accuracy and efficiency of animal detection processes.

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“…Jeevitha and Kumar (2019) have proposed an animal intrusion alert system based on the image processing techniques. This work targets intrusion related with only animals not the human [15].…”

Section: Related Workmentioning

confidence: 99%

A Lightweight Security Scheme (LSS) for Wireless Node Communication for Border Surveillance System

Singh

2021

JCSANDM

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The physical breach across the borders is a very common issue these days among nations sharing boundaries. It is controlled via proper border surveillance system. The border surveillance system is trivially a physical border intrusion detection system in which CCTV cameras are used traditionally to observe manually the presence of some intruder. Instead, we utilize the raspberry PI controller board based wireless sensor nodes fitted with raspberry PI camera for identifying the intruder. Once the intruder is identified, the wireless sensor nodes communicate the messages with the next hop sensor nodes and the message ultimately reaches the control room from where army action may be initiated. In this work, we propose a novel lightweight security scheme (LSS) for raspberry PI based wireless node communication for the Border Surveillance System. We have utilized the XBee (Zigbee) serial communication between raspberry PI based wireless sensor nodes. The proposed scheme is based upon the notion of confusion and correct identification of pattern (byte) in the transmitted messages. The entire communication scheme is lightweight and secure.

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A Review of Animal Intrusion Detection System

Cited by 13 publications

References 0 publications

Real-Time Threat Prevention System for Mitigating Intrusions by Dogs in Livestock Farming using IoT and Machine Learning

Real-Time Threat Prevention System for Mitigating Intrusions by Dogs in Livestock Farming using IoT and Machine Learning

A Comprehensive Review of Deep Learning Approaches for Animal Detection on Video Data

A Lightweight Security Scheme (LSS) for Wireless Node Communication for Border Surveillance System

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