Capturing fine resolution and well-calibrated video images with good object visual coverage in a wide space is a tough task for visual surveillance. Although the use of active cameras is an emerging method, it suffers from the problems of online camera calibration difficulty, mechanical delay handling, image blurring from motions, and algorithm un-friendly due to dynamic backgrounds, etc. This paper proposes a cell-based visual surveillance system by using N (N a parts per thousand yenaEuro parts per thousand 2) active cameras. We propose the camera scan speed map (CSSM) to deal with the practical mechanical delay problem for active camera system design. We formulate the three mutually-coupled problems of camera layout, surveillance space partition with cell sequence, and camera parameter control, into an optimization problem by maximizing the object resolution while meeting various constraints such as system mechanical delay, full visual coverage, minimum object resolution, etc. The optimization problem is solved by using a full searching approach. The cell-based calibration method is proposed to compute both the intrinsic and exterior parameters of active cameras for different cells. With the proposed system, the foreground object is detected based on motion and appearance features and tracked by dynamically switching the two groups of cameras across different cells. The proposed algorithms and system have been validated by an in-door surveillance experiment, where the surveillance space was partitioned into four cells. We used two active cameras with one camera in one group. The active cameras were configured with the optimized pan, tilt, and zooming parameters for different cells. Each camera was calibrated with the cell-based calibration method for each configured pan, tilt, and zooming parameters. The algorithms and system were applied to monitor freely moving peoples within the space. The system can capture good resolution, well-calibrated, and good visual coverage video images with static background in support of automatic object detection and tracking. The proposed system performed better than traditional single or multiple fixed camera system in term of image resolution, surveillance space, etc. We further demonstrated that advanced 3D features, such as 3D gazes, were successfully computed from the captured good-quality images for intelligent surveillance.