Video Semantic Segmentation and Tracking in Low Light Videos

低光视频中的视频语义分割和跟踪

• 批准号: 2894985
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2023
• 资助国家: 英国
• 起止时间: 2023 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2894985
• 关键词: Video; Semantic; Segmentation; Tracking; Low

Natural History filmmaking often requires filming in low-light environments which results in noisy, low-resolution and low-contrast videos. Due to the quality degradation, it can be difficult to do computer vision tasks like object detection and object tracking. These computer vision tasks are beneficial for production as they can automate and aid content acquisition and post-production. This research proposes to investigate various methods for video semantic segmentation and tracking in low-light videos to improve creative media production workflows. This research aims to explore the following research questions and problems: 1. Developing a semantic segmentation approach for object detection in the context of low-light videos 2. Developing a method that tackles that task of Video Semantic Segmentation (VSS) and Video Object Tracking (VOT) in low-light videos 3. Investigating the impacts of using various low-light enhancement techniques to improve semantic segmentation and tracking 4. Addressing the challenge of occlusion in object tracking specifically for low-light videos

自然历史电影制作通常需要在弱光环境下拍摄，这会导致视频嘈杂、低分辨率和低对比度。由于质量下降，执行对象检测和对象跟踪等计算机视觉任务可能会很困难。这些计算机视觉任务有利于制作，因为它们可以自动化并帮助内容采集和后期制作。本研究建议研究低光视频中视频语义分割和跟踪的各种方法，以改进创意媒体制作工作流程。本研究旨在探讨以下研究问题： 1. 开发一种用于低光视频背景下的对象检测的语义分割方法 2. 开发一种方法来解决低光视频中的视频语义分割 (VSS) 和视频对象跟踪 (VOT) 任务 3. 调查使用各种低光增强技术改进语义分割和跟踪的影响 4. 专门解决对象跟踪中遮挡的挑战 适用于低光视频