Hybrid Human-AI in Wildlife Image Recognition

Wildlife conservation relies on accurate species identification, but traditional monitoring methods face challenges due to the vast amount of camera trap images requiring manual annotation. This project introduces a Hybrid Human-AI Wildlife Image Recognition System that integrates deep learning with human expertise to improve classification accuracy and reduce annotation efforts. The system utilizes iterative learning, Open Long-Tailed Recognition (OLTR), and active learning to handle rare species and imbalanced datasets effectively. Through real-world validation in Gorongosa National Park, results show a significant reduction in manual intervention while enhancing model adaptability to novel species. The proposed approach bridges automation and human intelligence, making it scalable for conservation efforts and ecological research.