碩士 / 國立中央大學 / 資訊工程學系 / 103 / Video surveillance has widely applied in our daily life, both in public and private environments, such as schools, offices, shopping malls, streets, and homes; however,the view scope of a single camera is finite and limited by scene structures. In order to monitor a wide area and trace a complete trajectory of a moving object, multi-camera video surveillance systems received a lot of attention in recent years.
The view angleof a fisheye camera is 180 degree,so it can cover a wider field of view than a normal camera. Thus, in the same surveillance environment,only a fewfisheyecameras can replace many traditional cameras to survey the events; such that the cost of system construction and management are then reduced. In this thesis, we propose an automatic detection and tracking system with two fisheye cameras for environment surveillance.The proposed system is composed of two major modules: foreground detection and foreground tracking.
In the foreground detection module,background subtraction is used to detect foreground pixels and logical morphology is exploited to connect foreground pixels as blobs and remove noises. Shadow areas are removed based on the characteristics of shadow that is a small block in background image with a significant change inintensity. Background update mechanism can adapt to the rapid and slow light change.
Foreground tracking is accompanied with Kalman filtering for pedestrian motion prediction. A transform table is pre-established to associate multi-camera data in the overlapping areas.When objectacross disjoint camera views, the data in the lookup table can provide enough information to realize the moving object in camera views actually belonging to the same object,and keep consistent labels on the object.To improve the reliability of the tracking performance, motion and color appearance features are used to match the detected objects in different cameras.Every camera has its own trackerto trace multiple target trajectories even if the moving objects are partial and complete occluded.
We conducted experiments with the proposed system on several videos; the environments of these captured videosare varied in brightness and have different object numbers. Theexperiments results show that the average sensitivity is 96.7 percent and the average false positive rate is 0.45 percent because the foreground objects are similar to the background. The average sensitivity rises to 98.55 percent with the Kalman filter.It demonstrates that the proposed method can work well under challenging conditions, such as light change, shadow interference, objectocclusion.So the proposed joint detection and tracking system is effective andreliable in practice.
| Identifer | oai:union.ndltd.org:TW/103NCU05392079 |
| Date | January 2015 |
| Creators | Jia-Huei Tseng, 曾佳慧 |
| Contributors | Din-Chang Tseng, 曾定章 |
| Source Sets | National Digital Library of Theses and Dissertations in Taiwan |
| Language | zh-TW |
| Detected Language | English |
| Type | 學位論文 ; thesis |
| Format | 64 |
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