SBIR Phase I: Using Hidden Markov Models to Track Human Targets

SBIR 第一阶段：使用隐马尔可夫模型跟踪人类目标

• 批准号: 9760988
• 负责人: Scott Thompson
• 金额: $ 9.97万
• 依托单位: ChT Engineering Systems
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 1998
• 资助国家: 美国
• 起止时间: 1998-01-01 至 1998-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=9760988&HistoricalAwards=false
• 关键词: SBIR; Phase; Using; Hidden; Markov

This Small Business Innovation Research Phase I project will investigate the feasibility Of implementing a novel multi-sensor data fusion technique for low cost target tracking. Literature suggests that Hidden Markov Models significantly out perform other methods of multiple target tracking when ambiguous detections, noise, and clutter are present. The primary objective here is to demonstrate the feasibility of utilizing Hidden Markov Models to track human targets. The target tracking problem can be viewed as one of developing a model that minimizes the difference between the model's estimate of the target coordinates {Xout(t), Yout(t)} and the actual coordinates given by {Xactual(t), Yactual(t)}. In practice, this must be done simultaneously for a varying number of independent targets. However, this Phase I effort will focus on single target tracking and leave multiple target tracking to the subsequent Phase II project. Portable code used to implement models in Phase I will be incorporated in a microprocessor-based system following multiple target refinement in Phase II. Synthesized sensor data will be used initially to develop tracking models. These data will then be supplemented with measured data from ultrasonic sensors, in order to further refine the tracking model. Direct applications of the human tracking system include video surveillance for security, safety, and human or animal behavioral observation systems. With spotlight attachments the device will provide an illuminated path for remote search and rescue operations, emergency evacuation in poorly lit buildings, crime deterrence, and night surveillance tracking of escaping prisoners. This system will also be well suited as a supplement to a video target tracking and identification system. This award is supported by the Small Business Innovation Research Program and the Experimental Program to Stimulate Competitive Research (EPSCoR).

这个小型企业创新研究阶段I项目将调查实施一种新型的多传感器数据融合技术来进行低成本目标跟踪的可行性。 文献表明，当存在模棱两可的检测，噪音和混乱时，隐藏的马尔可夫模型会显着执行多个目标跟踪的其他方法。 这里的主要目的是证明利用隐藏的马尔可夫模型跟踪人类目标的可行性。 目标跟踪问题可以视为开发一个模型之一，该模型可以最大程度地减少目标坐标{xout（t），yout（t）}的估计与{xactual（t），yactual（t）}给出的实际坐标之间的差异。 实际上，对于不同数量的独立目标，必须同时完成此操作。 但是，I阶段的工作将集中在单个目标跟踪上，并将多个目标跟踪留给随后的II阶段项目。 在II阶段的多个目标改进之后，用于在第一阶段实现模型的便携式代码将被合并到基于微处理器的系统中。 合成的传感器数据最初将用于开发跟踪模型。 然后，这些数据将补充超声传感器的测量数据，以便进一步完善跟踪模型。 人类跟踪系统的直接应用包括针对安全，安全以及人类或动物行为观察系统的视频监视。 随着焦点附件，该设备将为远程搜索和救援行动提供一条照明途径，亮度较差的建筑物的紧急撤离，犯罪威慑以及逃脱囚犯的夜间监视跟踪。 该系统还将非常适合作为视频目标跟踪和标识系统的补充。 该奖项得到了小型企业创新研究计划和刺激竞争研究的实验计划的支持（EPSCOR）。