摘要
对于许多可视跟踪和视频分析任务,精确的摄像机标定是非常重要的。提出一种新的视频监控摄像机自标定算法,它利用半正定规划来恢复摄像机的焦距和主点。说明如何将摄像机旋转自标定算法转化为凸优化问题,该方法将所需正定约束自动集成到优化过程,因此得到可靠和稳定的结果。基于合成数据和真实图像的实验,证实了算法有效性和可确定的收敛性。
For many visual tracking and video analysis tasks, an accurate camera calibration is very important. A new camera auto-calibration method for video surveillance is presented. The novelty of the approach lies in the application of semidefinite programming for recovering the camera focal lengths and the principal point. This paper demonstrates how to re-formulate the rotation based camera auto-calibration as an convex optimization problem. The method automatically incorporates the required positive-definiteness constraint into the computation, obtaining more reliable and more stable results. The experimental results on both synthetic data and real images verify the effectiveness and guaranted convegenee of the algorithm.
出处
《南京邮电大学学报（自然科学版）》
2009年第4期31-34,共4页
Journal of Nanjing University of Posts and Telecommunications：Natural Science Edition
基金
南京邮电大学攀登计划(NY208050)资助项目
关键词
摄影机自标定
半正定规划
视频监控
凸优化
camera auto-calibration
semidefinite programming
video surveillance
convex optimization
