Cooperative Strategy and Algorithms of Surveillance System Integrated with Fixed Global-view Camera and Active Focused-view Cameras

In recent years, much research has been focused on functionality and feasibility of multi-camera surveillance system. This study describes two types of surveillance scenarios. One is the surveillance of public monitoring and the other is the surveillance of indoor environment with numerous stations.
In the type of surveillance of public monitoring in wide area, the proposed architecture is designed. On the aspect of surveillance system, the wider coverage guarantees the security of area. However, it is difficult to gather detailed information using the wide field-of-view (FOV) sensor due to its limitation in resolutions. In order to maintain the desired image resolution and still have a wide FOV, this requires the use of active camera. Thus, the current architecture design combined fixed global-view camera and active focused-view camera to make use of their advantage, respectively.
Furthermore, the methods to achieve multi-target object detection and tracking are proposed. In order to maintain the identity of a moving object, the trajectory of the center of mass (TCM) is proposed to accomplish this task of labeling. To coordinate two different sensors, the model of coordinate transformation is derived. Without the act of resource assignment, system redundancy is increased during the surveillance process. Hence, the system aims to reduce this redundancy by applying the cooperative strategy.
In the scenario of indoor environment with numerous stations (e.g., classroom, assemble line in factory, office), multiple observation points are required for visual sensors. The method of monitoring multiple points is proposed to improve the correctness of observed points for further information transmission with other global-view cameras.
The performance of motion detection algorithm is occasionally poor due to its fixed background updating rate. The problem with the fixed low background updating rate is that static object is not updated as the background since the transient time is short. However, with the fixed high background updating rate, the result of updating moving objects as the background is not desired. To improve the correctness of detecting result, motion detection with the adaptive background updating (ABU) is proposed. Finally, the experimental results of different scenarios are shown in this study.