Abstract: Instructional videos have become increasingly popular for sharing knowledge and providing step-by-step guidance in various domains, ranging from cooking and crafts to academic subjects and technical tutorials. However, these videos often contain a significant amount of visual content, making it challenging for users to quickly grasp the key information and instructions. To address this issue, the field of video summarization has emerged, aiming to automatically extract concise and informative summaries from instructional videos. This paper presents a comprehensive review and analysis of existing techniques for the summarization of visual content in instructional videos. We categorize the approaches into two main groups: frame-based and object-based methods. Frame-based methods focus on selecting key frames that represent the essential information in the video, while object-based methods aim to identify and summarize relevant objects or regions of interest within the video. We discuss various strategies employed by these methods, including visual saliency analysis, motion analysis, and semantic understanding. Furthermore, we explore the challenges associated with instructional video summarization, such as handling complex scenes, dealing with occlusions, and understanding temporal dependencies. We also highlight the evaluation metrics commonly used to assess the quality of video summaries, including content coverage, representativeness, and coherence. Additionally, we present existing benchmark datasets and discuss their limitations in capturing the diverse range of instructional videos. Finally, we provide insights into potential future research directions in this field, such as incorporating multimodal information, leveraging deep learning techniques, and exploring user preferences to personalize video summaries. By summarizing visual content in instructional videos effectively, we can enhance the accessibility and usability of these videos, allowing users to quickly grasp the key concepts and instructions. This survey serves as a valuable resource for researchers and practitioners interested in video summarization and lays the groundwork for further advancements in this area.

Keywords: Summarization, Visual Content, Instructional Videos, Video Summarization, Key Frames, Object-based Methods

| DOI: 10.17148/IARJSET.2023.105108