Real - Time Animal Detection in Forest Areas Using VGG-19 and BI - LSTM Techniques

Rural populations and forestry workers face significant safety risks due to inadequate surveillance methods for detecting animal types and movements. To address this challenge, this project introduces a hybrid VGG-19 and Bi-LSTM network aimed at enhancing safety monitoring in forested areas. The VGG-19 model excels in feature extraction, while the BiLSTM network specializes in learning sequential data, collectively enabling precise detection of animal types and behaviours. This integrated approach not only improves accuracy in identifying wild animals but also provides a cost-effective alternative to traditional surveillance techniques. The primary objectives of this project include improving the accuracy of animal detection and activity classification in forested regions by leveraging the strengths of VGG-19 and Bi-LSTM algorithms. Furthermore, the project seeks to mitigate the high computational expenses associated with existing surveillance methods through model optimization, ensuring both cost-effectiveness and superior detection performance. The efficiency and accuracy of the proposed VGG-19 + Bi-LSTM model is rigorously evaluated against traditional Convolutional Neural Network (CNN) approaches, offering valuable insights into the advancements and benefits of the new methodology. By incorporating advanced deep learning techniques, the proposed system ensures comprehensive monitoring of forest animal activity, enhancing safety measures. Through meticulous comparison with existing CNN models, the system identifies the most effective method for real-time animal activity detection, facilitating optimized surveillance implementation. This project aims to ensure the safety of individuals in rural and forested areas, delivering improved safety measures and peace of mind through accurate and reliable animal detection.