Volume 18, No. 6, 2021

Animal Detection And Alert System Using Computer Vision Technique

Ponmani R


The integration of computer vision techniques has revolutionized the field of object detection, enabling the development of intelligent systems for various applications. This paper presents an Animal Detection and Alert System leveraging the Speeded-Up Robust Features (SURF) algorithm in computer vision. The proposed system aims to detect and recognize animals in real-time by analyzing video streams or images captured through cameras. The SURF algorithm, known for its robustness to changes in scale, rotation, and illumination, is utilized for feature extraction, enabling the identification of distinctive patterns and keypoints within images. These extracted features serve as the basis for training a machine learning model, facilitating the classification and recognition of different animal species. The system is designed to operate in diverse environmental conditions and is adaptable to various species, accommodating a wide range of animal shapes, sizes, and appearances. Upon successful detection, the system triggers an alert mechanism to notify users or relevant authorities, contributing to the timely and effective management of wildlife crossings, habitat monitoring, or animal presence in restricted areas. This research outlines the methodology employed in dataset collection, preprocessing, feature extraction using SURF, model training, real-time implementation, and alert mechanisms. Furthermore, it addresses the challenges encountered in deploying such systems and proposes avenues for future enhancements, including optimizing performance, expanding the dataset, and exploring alternative algorithms for improved accuracy and efficiency. The proposed Animal Detection and Alert System using omnipotent classifier which demonstrates promising capabilities in enhancing wildlife conservation efforts, minimizing human-wildlife conflicts, and supporting ecosystem monitoring, contributing significantly to the field of computer vision applications in wildlife management and environmental conservation.

Pages: 9321-9328

Keywords: Speeded-Up Robust Features, Animal Detection, Alert System, Object Detection, Omnipotent Classifier, Scale, Rotation, and Illumination.

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