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Untrimmed videos have interrelated events, dependencies, context, overlappingevents, object-object interactions, domain specificity, and other semanticsthat are worth highlighting while describing a video in natural language. Owingto such a vast diversity, a single sentence can only correctly describe aportion of the video. Dense Video Captioning (DVC) aims at detecting anddescribing different events in a given video. The term DVC originated in the2017 ActivityNet challenge, after which considerable effort has been made toaddress the challenge. Dense Video Captioning is divided into three sub-tasks:(1) Video Feature Extraction (VFE), (2) Temporal Event Localization (TEL), and(3) Dense Caption Generation (DCG). This review aims to discuss all the studiesthat claim to perform DVC along with its sub-tasks and summarize their results.We also discuss all the datasets that have been used for DVC. Lastly, wehighlight some emerging challenges and future trends in the field.

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