# Cell-Based Target Localization and Tracking with an Active Camera
## 2 Related Work
### Device-Level (PTZ Camera) Target Tracking  
[PERSON] et al.'s [37] method uses two PTZ cameras to observe moving targets in the path, such as alternate channels. The experimental scene is divided into several continuous cells, and then each cell is calibrated. The PTZ camera quickly calibrates online after the operation and then tracks the moving targets in the cell for observation.  
In [PERSON] et al.'s method [38], the area of interest is first manually selected, and then the three features of the feature points in the area are extracted. The features include the mean, the variance, and the range of intensity values. Then, they calculate the transformation matrix with the information of the feature points and the K-means algorithm for the purpose of tracking the target of interest in the image.  
[PERSON] et al. [39] proposed a target tracking model based on a PTZ camera. The model consists of background modeling, observation frame registration, and target tracking. The first step is to build a background model for the observation scene. Then, the feature extraction algorithm is used to register the region of interest in the adjacent frames; finally, the classic target tracking algorithm is used to cooperate with the PTZ camera to achieve the purpose of actively tracking the target in the scene. This method makes full use of the advantages of the PTZ camera and combines the image-level target tracking algorithm to realize the tracking of the scene target.  
[PERSON] et al. [40] built a moving target tracking system based on a PTZ camera. The system includes three parts: adaptive background generation, moving region extraction and tracking. Similar to the method proposed by [PERSON] et al., it also needs to build a background model of the scene first, and then extract the moving target from the continuous frame. Finally, we changed the PTZ parameters of the active camera for the tracking target.  
[PERSON] et al. [41] proposed an online calibration method for a PTZ camera. First, the internal and external parameters of the camera were calibrated offline before the operation. Then, when the PTZ camera started working, the shooting attitude of the current camera was calculated by extracting the captured image features, and then it was compared with the external parameters of the calibrated camera. Following these steps, the camera's attitude was adjusted to track and observe the target.  
[PERSON] et al. [42] used a dual-camera system to locate and track small targets in the scene. This method constructs a camera model that extracts the target features to calculate the best posture. We adjusted the parameters of the PTZ camera to track and observe the target.  
[PERSON] et al. [43] constructed a dual-camera system for target tracking and observation. The dual-camera system includes an omnidirectional camera and a PTZ camera. First, the omnidirectional camera provides a 360-degree field of view to locate and classify the target. Then, the PTZ camera is used to track the target.  
[PERSON] et al. [44] constructed a PTZ camera observation model, namely a four-cell-based image target tracking method. They achieved image target tracking by one PTZ camera. However, the method cannot precisely locate the target and has a significant tracking error.