CAREER: Elements in Solving the Multiple Object Tracking Problem

职业：解决多目标跟踪问题的要素

• 批准号: 0348020
• 负责人: Hai Tao
• 金额: $ 50万
• 依托单位: University of California-Santa Cruz
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-02-01 至 2009-01-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0348020&HistoricalAwards=false
• 关键词: CAREER; Elements; Solving; Multiple; Object

The proposed project addresses several challenging issues in multi-object tracking such as failure modes, occlusion, background clutter, varying object appearance, multi-object confusion, and high computational complexity. This project will investigate five key elements in multi-object tracking including (1) a trajectory tracking framework, (2) a joint multi-object representation, (3) image-based likelihood functions, (4) the integration of object detection in tracking, and (5) the compact and efficient discriminative feature tracking. Based on recent preliminary results, it is expected that significant improvements in multi-object tracking will be achieved by integrating these elements into the proposed probabilistic estimation framework. An experimental system for tracking multiple pedestrians and vehicles will be implemented for testing the developed algorithms. The proposed educational program will focus on developing graduate and undergraduate courses in the area of computer vision, establishing an active research laboratory in visual computing and human-computer interaction, and promoting collaboration with the industry to offer students the opportunities to work on real-world research problems.Success of this project will provide a novel solution to the multi-object tracking problem and significantly advance the state-of-the-art. The proposed project is expected to have broader impacts in the areas of video surveillance, human-computer interaction, and intelligent teleconferencing. The proposed research will also have an impact on establishing an educational program in computer vision at the University of California, Santa Cr

该项目解决了多目标跟踪中的几个具有挑战性的问题，如故障模式，遮挡，背景杂波，不同的目标外观，多目标混淆和高计算复杂性。本计画将探讨多目标追踪的五个关键要素，包括：（1）轨迹追踪架构、（2）联合多目标表示、（3）以影像为基础的似然函数、（4）整合追踪中的目标侦测、以及（5）精简且有效的判别式特征追踪。根据最近的初步结果，预计将实现将这些元素整合到建议的概率估计框架，在多目标跟踪的显着改善。一个实验系统，用于跟踪多个行人和车辆将实施测试所开发的算法。拟议的教育计划将侧重于开发计算机视觉领域的研究生和本科生课程，建立一个活跃的视觉计算和人机交互研究实验室，并促进与业界的合作，为学生提供研究实际问题的机会。该项目的成功将为多目标跟踪问题提供一种新的解决方案，并显着推进该项目预计将在视频监控、人机交互和智能电话会议等领域产生更广泛的影响。这项研究也将对加州大学Santa Cr分校建立计算机视觉教育项目产生影响