# Cell-Based Target Localization and Tracking with an Active Camera
## 1 Introduction  
With the continuous advancement of science and technology and the continuous improvement of people's living standards, video surveillance has played an increasingly important role in people's lives [1, 2, 3, 4]. The traditional monitoring method based on a fixed perspective can no longer meet people's increasing demand for security protection. Therefore, PTZ cameras with rotation, pan, and zoom functions have gradually become a focus of people's attention [5, 6, 7, 8]. A camera with a PTZ function tracks and observes the target in the scene by changing its own parameters. However, due to the constantly changing position, speed, size, and background of the moving target in the scene, it is easy to cause a situation where the target cannot be accurately located, or the tracking target is lost. Recently, domestic and foreign researchers have conducted many related studies, but there are still many key issues to be urgently solved [9].  
The PTZ camera has the function of real-time perception of the external characteristics of the target in the scene. However, if one wants to further perceive other target characteristics, such as type, location, and speed, a new observation model must be built.  
To use the PTZ camera to obtain accurate target information such as type, position (speed), etc., we propose a target localization and tracking method based on cell and active cameras. In this method, we divide the area of target movement into multiple cells. We then use deep learning-based algorithms to identify the vertices and targets of the cells in the scene, and then use the PnP algorithm to calculate the world coordinates of the targets in the image. Finally, based on the target's world coordinates, the PTH model is used to calculate the \(P\) and \(T\) values of the PTZ camera, in order to achieve the purpose of tracking and observing the target.  
This experiment was conducted with a wireless remote-control Jeep (hereafter referred to as \"WRC-Jeep\") as the target and it was compared with other target tracking methods. The main contributions of this article are summarized as follows:  
(1) A cell segmentation and recognition method is proposed for the first time. This method divides the experimental scene into multiple cell areas, calibrates each cell's vertices, and then uses the YOLOv4 detection algorithm to identify the vertices of the cells and the target in the image. Finally, the proposed vertex matching method is used to match the vertex in the image and vertex in the scene.  
(2) A method for accurately calculating the target position is proposed first. Firstly, we used the PnP model to calculate the target's initial position. Secondly, we used the proposed error model to calculate the target error position. Finally, we used the target position to subtract the error position to obtain the precise target position.  
(3) A PTH model is constructed. The world coordinates can be transformed into \(P\) value, \(T\) value and \(H\) value based on the PTZ camera.  
The rest of this paper is organized as follows. Section 1 surveys works that are closely related to our method. Section 2 presents the proposed method in detail. The experimental results are demonstrated in Section 3. Finally, Section 4 concludes the paper.