Integrating object segmentation for robust object tracking

集成对象分割以实现稳健的对象跟踪

• 批准号: RGPIN-2017-04801
• 负责人: Amer, Aishy
• 金额: $ 1.75万
• 依托单位: Concordia University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2017
• 资助国家: 加拿大
• 起止时间: 2017-01-01 至 2018-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=637493
• 关键词: Integrating; object; segmentation; robust; tracking

Video object tracking is an active research field with applications diverse as augmented reality, self-navigation systems (drones or self-driving cars), human-computer interactions, hyper-linked social video, or athlete performance monitoring. Tracking is performed on raw video signals, captured typically from a single camera; different types of objects can be tracked, such as persons, athletes, vehicles, animals, or ships. Tracking of objects across real-world video signals has many challenges such as variations in object appearance due to scale change, occlusion, articulation, deformation, and interactions of multiple objects.

视频对象跟踪是一个活跃的研究领域，其应用范围广泛，包括增强现实、自主导航系统（无人机或自动驾驶汽车）、人机交互、超链接社交视频或运动员表现监测。跟踪在原始视频信号上执行，通常从单个摄像机捕获；可以跟踪不同类型的对象，如人、运动员、车辆、动物或船舶。在真实世界的视频信号中跟踪对象有许多挑战，例如由于规模变化、遮挡、衔接、变形和多个对象的相互作用而导致的对象外观变化。