SBIR Phase I: New Algorithms for PTZ Camera Based Object Tracking

SBIR 第一阶段：基于 PTZ 摄像机的对象跟踪的新算法

• 批准号: 0340283
• 负责人: Laura Drake
• 金额: $ 10万
• 依托单位: JunTech, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-01-01 至 2004-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0340283&HistoricalAwards=false
• 关键词: SBIR; Phase; New; Algorithms; PTZ

This Small Business Innovation Research (SBIR) Phase I research project will investigate and evaluate a new class of moving object-tracking algorithms for PTZ (pan-tilt-zoom) cameras in video surveillance systems. In most of today's video surveillance systems, real-time object tracking is performed manually by human operators using PTZ cameras. This is often stressful and inefficient (an operator can only control one PTZ camera at a time) and causes inconsistent results. The proposed project will investigate a new class of algorithms to direct a PTZ camera to track an object of interest automatically. This is done by using an optimal filter with new object and observation models. The project outcome will be software modules that can be integrated into standard video surveillance systems to improve their capabilities.Video surveillance systems are important tools in the fight against crime and terrorism. Most of the systems on the market today are relatively standard DVR's (digital video recorders) with few smart features. The proposed innovation (automatic object tracking) is a smart feature that can significantly improve a standard system's capabilities by allowing it to get better and more useful images. Since this feature is demanded by many end-users, it is highly attractive to equipment vendors and integrators. Hence, it has commercial potential. Finally, by introducing new models for object tracking (detailed in the Project Description), the proposed innovation also advances the state-of-the-art in image processing and computer vision research.

该小型企业创新研究 (SBIR) 第一阶段研究项目将研究和评估视频监控系统中用于 PTZ（平移倾斜变焦）摄像机的新型移动对象跟踪算法。在当今的大多数视频监控系统中，实时对象跟踪是由操作员使用 PTZ 摄像机手动执行的。这通常压力很大且效率低下（操作员一次只能控制一台 PTZ 摄像机）并导致结果不一致。拟议的项目将研究一类新的算法来指导 PTZ 摄像机自动跟踪感兴趣的物体。这是通过使用具有新对象和观察模型的最佳过滤器来完成的。该项目的成果将是可以集成到标准视频监控系统中的软件模块，以提高其功能。视频监控系统是打击犯罪和恐怖主义的重要工具。当今市场上的大多数系统都是相对标准的 DVR（数字录像机），几乎没有智能功能。所提出的创新（自动对象跟踪）是一项智能功能，可以通过允许标准系统获得更好、更有用的图像来显着提高标准系统的功能。由于许多最终用户都需要此功能，因此它对设备供应商和集成商具有很大的吸引力。因此，它具有商业潜力。最后，通过引入新的对象跟踪模型（在项目描述中详细介绍），所提出的创新还推动了图像处理和计算机视觉研究的最先进水平。