Video Semantic Segmentation and Tracking in Low Light Videos

自然历史电影制作通常需要在低光环境下拍摄，这会导致噪音，低分辨率和低对比度的视频。由于质量下降，很难进行目标检测和目标跟踪等计算机视觉任务。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内容获取和后期制作。本研究旨在研究各种用于低光视频语义分割和跟踪的方法，以改善创意媒体制作工作流程。本研究旨在探讨以下研究问题和问题：1.开发一种语义分割方法，用于弱光视频背景下的对象检测2.开发一种方法，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的影响，以改善语义分割和跟踪4。解决低光视频中对象跟踪中的遮挡问题

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