Persistent Tracking

持续追踪

• 批准号: 0535324
• 负责人: Robert Collins
• 金额: $ 30.15万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2005
• 资助国家: 美国
• 起止时间: 2005-08-01 至 2009-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0535324&HistoricalAwards=false
• 关键词: Persistent; Tracking

Current video-based trackers can track robustly over thousands of video frames. This work seeks to develop trackers that operate over two orders of magnitude more time (hours). Such long-term object tracking must be resilient to large changes in appearance of both the object and the surrounding environment. This requires raising the level of abstraction at which the tracker represents its target -- the goal must be tracking "objects", not image templates or distributions of color. The intellectual merit of this effort is to achieve persistent object tracking through novel research that spans the areas of on-line feature selection, foreground/background segmentation, and object model learning and recognition. Flexible appearance-based object descriptors are developed that automatically adapt to changes in object and background appearance. Shape-constrained figure/ground segmentation is performed to avoid model drift during adaptation. Object models are learned on-the-fly during tracking and used to search for and recognize the same object again after occlusion or tracking failure. Development of this technology for persistent object tracking has broad impact in commercial applications such as traffic monitoring, motion capture and automated surveillance, as well as law enforcement and military applications in trailing suspects and combatants. This project promotes scientific repeatability, code-sharing and dissemination of results by maintaining a tracking evaluation web site that provides open source tracking code, benchmark datasets, and a mechanism for online evaluation.

当前基于视频的跟踪器可以稳健地跟踪数千个视频帧。 这项工作旨在开发运行时间（小时）增加两个数量级的跟踪器。 这种长期对象跟踪必须能够适应对象和周围环境外观的巨大变化。这需要提高跟踪器表示其目标的抽象级别——目标必须是跟踪“对象”，而不是图像模板或颜色分布。 这项工作的智力价值是通过跨越在线特征选择、前景/背景分割以及对象模型学习和识别领域的新颖研究来实现持久对象跟踪。 开发了灵活的基于外观的对象描述符，可以自动适应对象和背景外观的变化。 执行形状约束的图形/地面分割以避免适应过程中的模型漂移。对象模型是在跟踪过程中动态学习的，并用于在遮挡或跟踪失败后再次搜索和识别同一对象。 这种持久目标跟踪技术的开发对交通监控、动作捕捉和自动监视等商业应用以及追踪嫌疑人和战斗人员的执法和军事应用产生了广泛的影响。 该项目通过维护一个提供开源跟踪代码、基准数据集和在线评估机制的跟踪评估网站来促进科学可重复性、代码共享和结果传播。