Key Frame

Key frame extraction aims to select the most representative frames from a video, summarizing its content efficiently. Current research focuses on developing automated methods using deep learning architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, often incorporating techniques like clustering and attention mechanisms to improve accuracy and efficiency. These advancements are crucial for various applications, including video summarization, compression, retrieval, and enabling more efficient analysis in fields such as sports analytics and medical diagnostics where processing large video datasets is a significant challenge. The development of robust and efficient key frame extraction methods continues to be a vital area of research in computer vision.