Persistent Tracking

目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超过两个数量级（小时）的跟踪器。 这种长期的对象跟踪必须对对象和周围环境的外观的大的变化有弹性。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。 这一努力的智力价值是通过跨越在线特征选择，前景/背景分割，对象模型学习和识别领域的新研究来实现持久的对象跟踪。 开发了灵活的基于外观的对象描述符，其自动适应对象和背景外观的变化。 进行形状约束的图形/地面分割，以避免在适应过程中的模型漂移。对象模型在跟踪过程中动态学习，并用于在遮挡或跟踪失败后再次搜索和识别相同的对象。 这种用于持续目标跟踪的技术的发展在商业应用中具有广泛的影响，例如交通监测、运动捕捉和自动监视，以及在跟踪嫌疑人和战斗人员方面的执法和军事应用。 该项目通过维持一个提供开放源码跟踪代码、基准数据集和在线评价机制的跟踪评价网站，促进科学重复性、代码共享和成果传播。

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.