Text-Driven Data Augmentation Tool for Synthetic Bird Behavioural Generation

Environmental conservation and biodiversity monitoring efforts have been greatly enhanced by the use of deep learning and computer vision technologies, particularly in protected areas such as parks and wildlife reserves. However, the development of accurate species recognition and behaviour understanding models is limited by the lack of detailed action annotations and reliance on static images, despite the availability of several animal datasets. This paper explores the use of generative models to tackle the challenge of creating comprehensive and diverse synthetic video data for identifying bird species and their behaviour. By utilising and fine-tuning different generative models, this study assesses the feasibility of synthetic video data generation to overcome these limitations. Our work focuses on generating realistic and varied video sequences that can improve machine learning algorithms for bird action detection and species recognition, thereby contributing to the protection and management of natural habitats. Throughout this investigation, we aim to provide new methodologies and tools for wildlife conservation technology, enhancing the monitoring and safeguarding of bird populations in their natural environments.