An Approach for Wild Animal Detection in Videos by using Deep Learning

Due to land availability decreases and rapid urbanisation many wild animals near the forest areas comes out for water and food. Wild animal detection strives to address the critical issue of manual surveillance challenges faced by forest officers and conservationists in vast natural habitats. Traditional methods, such as periodic surveys and camera traps, have proven inadequate in providing real-time data and comprehensive coverage, impeding effective conservation efforts. Moreover, budget constraints and the absence of automation further exacerbate these challenges. In response, our project proposes an innovative solution that seamlessly integrates deep learning and instant messaging technologies, fostering affordable and continuous surveillance. By harnessing edge computing and freely available messaging channels, our system aims to significantly enhance real-time visibility and data-driven decision-making in conservation. The primary goal is to empower conservationists with timely and actionable information for informed decision-making. Through the deployment of advanced deep learning algorithms, our system can recognize and track wildlife activity, triggering instant notifications to forest officers and relevant stakeholders via instant messaging platforms.