Video summarization generates compact representations of videos in the form of summaries. The proposed framework is a unified model for instance-driven egocentric video summarization addressing generic and query-based summarization along with multi-video summarization. The model employs deep learning for object detection and semantic web technologies in the form of ontologies for query inferences. Combining user preferences in the form of object queries has aided in producing summaries that are subjective in nature. Quantitative evaluations performed on two novel datasets namely, ‘vehicle expo’ and ‘academic inspection’ prove that the proposed framework produces remarkable results with the employment of instance-driven modules for summarization. Additional experimental analysis for shot boundary detection have been conducted based on proposed method and conventional methods establishing the significance of the instance-based model. Moreover, qualitative evaluations further ensure that the summaries are concise, representative, diverse and semantically relevant further substantiating the need for instance-driven models in video summarization.

视频摘要以摘要的形式生成视频的紧凑表示。所提出的框架是用于实例驱动的以自我为中心的视频摘要的统一模型，该模型解决了通用和基于查询的摘要以及多视频摘要。该模型将深度学习用于对象检测和语义网络技术，并以本体的形式进行查询推理。以对象查询的形式组合用户首选项有助于产生本质上是主观的摘要。对“车辆博览会”和“学术检查”这两个新颖的数据集进行的定量评估证明，所提出的框架通过使用实例驱动的模块进行汇总而产生了显着的结果。基于提出的方法和常规方法进行了镜头边界检测的其他实验分析，建立了基于实例的模型的重要性。此外，定性评估还确保摘要是简洁，代表性，多样且在语义上相关的，进一步证实了视频摘要中对实例驱动模型的需求。