Dense Video Captioning: A Survey of Techniques, Datasets and Evaluation Protocols

More recently, developing 2D and 3D convolutional neural networks (CNNs) has sparked interest in studying static and dynamic visual media's encoding, captioning, and query-answering capabilities. However, accomplishing these tasks on long, unedited video significantly challenges computer vision. Dense video captioning aims to make a computer understand what is happening in a video and establish a relation between the video content and its meaningful natural language description. The capability of describing events in videos aids a variety of systems, including blind navigation, video searching, surveillance, medical image analysis, and automatic video subtitling. The urge to detect captions on images and videos started in 1970 when researchers began working with images and video snippets containing captions. The art of displaying text on images and video transcribing the audio is called closed captioning. To serve the consumers who are hard of hearing and to take part in technology improvement motivated researchers to develop some automatic caption detection systems [92, 152].