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Video description is the automatic generation of natural language sentencesthat describe the contents of a given video. It has applications in human-robotinteraction, helping the visually impaired and video subtitling. The past fewyears have seen a surge of research in this area due to the unprecedentedsuccess of deep learning in computer vision and natural language processing.Numerous methods, datasets and evaluation metrics have been proposed in theliterature, calling the need for a comprehensive survey to focus researchefforts in this flourishing new direction. This paper fills the gap bysurveying the state of the art approaches with a focus on deep learning models;comparing benchmark datasets in terms of their domains, number of classes, andrepository size; and identifying the pros and cons of various evaluationmetrics like SPICE, CIDEr, ROUGE, BLEU, METEOR, and WMD. Classical videodescription approaches combined subject, object and verb detection withtemplate based language models to generate sentences. However, the release oflarge datasets revealed that these methods can not cope with the diversity inunconstrained open domain videos. Classical approaches were followed by a veryshort era of statistical methods which were soon replaced with deep learning,the current state of the art in video description. Our survey shows thatdespite the fast-paced developments, video description research is still in itsinfancy due to the following reasons. Analysis of video description models ischallenging because it is difficult to ascertain the contributions, towardsaccuracy or errors, of the visual features and the adopted language model inthe final description. Existing datasets neither contain adequate visualdiversity nor complexity of linguistic structures. Finally, current evaluationmetrics ...

Video Description: A Survey of Methods, Datasets and Evaluation Metrics