## 2 Literature Review
### Discussion
#### 2.2.3 Stationary Camera Networks
Each provides unique attributes of a smoke image that enhances the classification process. The next steps involve choosing classifiers, which can be of many typical sorts such as: k-nearest-neighbor, neural networks, support vector machines, etc. [76]. The models programmed in the cameras are trained and tested before deployment. The decoupling of the capturing and processing provides the advantage of continuous improvement and integration as researchers can update the models and analyze past performance without having to alter any of the physical components or interrupt its ongoing operation. For example, by basing their project on cloud computation [83], [PERSON] et al., have continued to release improvements to their iForestFire semi-automatic monitoring system after its commercial deployment and operations in various regions. Many techniques have successfully utilized smoke detection in early wildfire detection prototypes; yet, researchers continue to refine and improve the process. A common  
Figure 5: Typical application of camera networks paired with AI. (Images sourced from Creative Commons, following the guidelines on Attribution 3.0 Unported, CC By 3.0 [26]. The only modifications to the images are cropping and resizing). weakness in smoke detection and image-based detection is deterioration of image quality at night and by the presence of haze. Haze is defined as an \"an obscurity occurring due to smoke, dust, or other particles suspended in the atmosphere\" [83]. This causes images and colors to become fuzzy or blurry, decreasing contrast between elements of the image and therefore increasing the difficulty of identifying smoke. Some examples of haze are fog and air pollution. To address this, [PERSON] et al., utilized a de-hazing technique known as \"dark channel prior\". It reconstructs haze-reduced images with equations generated, with the knowledge that (1) most haze-free non-sky patches of an image have low or zero intensity in at least one color channel, and (2), that the intensity of these patches is increased by the presence of haze [84]. Like haze, the lack of light significantly diminishes the usability of captures taken at night. Inadequate lighting prevents distinguishing of colors and behaves like noise, often causing fire misclassification with other light sources such as car lights or lanterns. To combat this, [PERSON] et al., extract smoke temporal features and motion in addition to spatial features, considering factors such as flickering and flaring [85]. With the extra information, their process uses two machine-learning stages: the GoogLeNet CNN first extracts spatial features to then pass onto a bidirectional long short-term memory network (BLSTM), which learns the temporal relationships in the data [85]. The models' decisions are then congregated and ultimately decided through majority voting [85]. _Advantages:_  
Camera networks are largely autonomous and positioned farther away from the site of potential fires, unlike sensor networks and some UAVs. Therefore, there are little opportunities for them to receive any sort of damage from fires. Although considerations still need to be taken in the camera's placement, it is simplified by the longer range and more straightforward operation when compared to sensor nodes, whose low coverage and various short-range sensors necessitate more planning. _Challenges:_  
The relatively large coverage of a stationary camera partially alleviates, but does not eliminate, the constraint of a required continuous power source, as video feeds or frequent captures are power consuming. Consequently, it may be impossible to use these systems in an extremely remote area. The setup of the camera towers also needs an initial investment of resources and manpower for building the towers, and, therefore, might not be economically feasible in many geographical locations. Due to this positioning of the camera, any sensors considered for inclusion in the camera system may also need to have similar range or function as the cameras, limiting possible additions.