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中拍摄，从而导致嘈杂，低分辨率和低对比度视频。由于质量下降，可能很难执行计算机视觉任务，例如对象检测和对象跟踪。这些计算机视觉任务对生产是有益的，因为它们可以自动化并帮助获取和后期制作。这项研究建议调查低光视频中视频语义细分和跟踪的各种方法，以改善创意媒体的生产工作流程。这项研究旨在探索以下研究问题和问题：1。在低光视频的背景下开发一种语义分割方法以检测对象检测2。开发一种方法，以在低光视频中处理视频语义分割（VSS）和视频对象跟踪（VSS）的任务（VSS）和低光视频中的这项方法3。使用较低的较低范围进行挑战，以改善较低的挑战技术的影响。用于弱光视频