## 2 Wireless Multimedia Sensor Networks
### Directional Sensor Coverage
#### 2.4.1 Accurate Camera Calibration  
Camera calibration is necessary for mapping imagery metrics and coordinates into real world metrics and coordinates. Depending on the parameters being calibrated, camera calibration can be classified into intrinsic camera calibration and extrinsic camera calibration as summarized in Figure 5.  
* Intrinsic camera calibration (ICC): ICC refers to the computation of internal camera parameters for transforming imagery metrics to real world metrics, for example pixel distance to real world physical distance [79; 80]. Depending on the assumptions made upon the scene structures used for calibration, ICC can be carried out either with assistance of reference targets or target-free under self-calibration [81]. Oftentimes reference targets are used when it is important to obtain high accuracy of calibration [82]. Common targets placed in the scene can be 1D objects [83], 2D objects [84], and 3D objects [81], with calibration accuracy and complexity both increase as the targets get more sophisticated. Self-calibration, on the other hand, requires no reference targets but rather relies on the static scene structure and camera motion [85]. With no assumptions made on the scene structure, however, self-calibration needs to estimate a large number of parameters and quite often the correlation between these parameters lowers accuracy of calibration [86].
* Extrinsic camera calibration (ECC): With ECC, we can calculate the external parameters of a camera sensor. Such information is used for converting imagery coordinates to real world coordinates [80; 87], which is crucial in order to make the imagery data geographically meaningful. The decisions on ECC can be told apart from three criteria. First, depending on where the calibration actually takes place ECC can be carried out either in a centralized or a distributed fashion [79]. When centralized ECC is executed, imagery tracking data captured by all camera sensors are sent to the sink, with which the sink calculates the extrinsic parameters for all [88]. In distributed ECC however, each camera sensor determines its own extrinsic parameters [79; 80; 89]. Second, judging by whether overlapped FoVs exists or not different ECC approaches can be deployed [90]. When there exists overlapped FoVs, pairwise calibration is usually used first to find correspondences in the overlapped FoVs. An optimization procedure is then adopted to obtain the best matching correspondence [91; 92]. On the other hand, when no overlapped FoVs present, prior knowledge on the moving target (e.g., velocity) and trajectory prediction are needed for calibration [88; 93]. Third, depending on the degree of constraints placed on the calibration environment, ECC can either be supervised or fully automated. Most ECC techniques under current literature fall in the supervised category. They demand a priori of the target, such as location of the target [79; 93], height of the target [92], or the target's motion dynamics [88; 93; 117]. On the contrary, in fully automated ECC, no assumptions on the target or the environment are necessary [87; 94].