A Study on Object Tracking Method for Master-Slave Imaging Surveillance System
| Autor: | Wei-Chen Zhao, 趙偉辰 |
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| Rok vydání: | 2014 |
| Druh dokumentu: | 學位論文 ; thesis |
| Popis: | 102 Visual surveillance system has various applications and it is an important research topic in computer vision. There are some issues of these systems on expanding field of view (FOV) and enhancing the resolution of images. Hence, the master-slave imaging systems which can provide large FOV and also high resolution images simultaneously have been applied to pedestrian surveillance, access control, and crowd statistical analysis. The master-slave imaging system is a combination of two cameras: a master camera and a slave camera. The master camera has large FOV and is responsible for monitoring and object tracking. The slave camera, pan-tilt-zoom camera, is then guided by the master camera to rotate and zoom in the targeted object to acquire high resolution images. As the targeted objects are not always stationary, the objects’ motion behavior estimation is required for tracking the correct object and acquiring zoom-in images. In reality, objects have much more complex interactions and may cause object tracking failures. In this research, data from multiple cameras in master-slave imaging system were integrated to track and predict multiple objects’ behavior. The individual object was identified and matched for occlusion handling. The multiple object tracking results show a significant improvement on the master-slave imaging system’s robustness. The system was tested at an outdoor environment in National Taiwan University campus and the Taipei Zoo. It can track and record the high resolution image sequence and trajectory of targeted object for later offline analysis. The master camera provides a large FOV of about 195 degrees. The object tracking successful rate was about 74% ~ 77% and the resolution of targeted object’s image can be zoomed to about 35 times. The video frame rate speed could achieve 10 fps. |
| Databáze: | Networked Digital Library of Theses & Dissertations |
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