## 2 Literature Review
### Discussion
#### 2.2.3 Stationary Camera Networks  
Camera networks consist of advanced and feature-rich, interconnected cameras that monitor a vast area for fires (Figure 5). Early camera networks consisted of live camera images or videos streamed to a control room, where an operator would manually scan the feeds for signs of fire. In modern camera networks, the cameras are still the main drivers of the system, but are often partered with other systems, such as AI and communication servers, to fully optimize the camera to the desired area. Some may also begin as a single, standalone camera with the option of expansion through additional cameras. They can be stationed both in urban and remote areas and combat fires through early detection and prediction. While stationary, the cameras are usually free to pan in wide or complete 360\({}^{\circ}\) angles, significantly increasing coverage zone per camera and limiting blind spots. In addition to video feed, the cameras may also include different types of IR and thermal to enhance detection ability by broadening the features captured. The integration of AI enables them to be autonomous, allowing them to succeed the bulkier, man-powered and more intrusive watch towers [76]. This allows the cameras to not only capture images of the surroundings, but to independently identify if the captures are indicative of a fire. The inclusion of internet access grants the system the ability to communicate, allowing them to broadcast their feeds to a network which directly notifies specified users of notable events. For example, a camera may capture a column of smoke an abnormality that its AI programming classifies as a probable fire. It then relays the information to a central processor, the component that notifies authorities through an app [77]. A widely implemented example of camera networks is ALERTWildfire, a collaborative effort between The University of Nevada (Reno), University of California San Diego and the University of Oregon [78]. Hundreds of ALERTWildfire cameras have been installed in the southwest and west United States and assisted with real-time monitoring of fires. In 2021, Pacific Gas and Electric (PG&E) installed AI-equipped ALERTWildfire cameras in northern and central California, in collaboration with the AI company Alchera [79]. A San Francisco company founded in 2019, Pano AI, also uses AI on HD camera video feeds to automatically detect wildfires and reduce the response time to fire events [80]. IQ-Firewatch, a Portuguese company, employs AI as well, but also provides customization and scalability to serve a variety of settings [81]. Their cameras, commercially sold, are optionally attached with sensors like monochrome, RGB, near-IR, or thermal IR and can smoothly work with cameras added in the future. Its method of wildfire prevention revolves around capturing high-quality raw images with cameras and processing them in real-time using smoke detecting algorithms [81]. Detecting fires at the earliest signs of smoke may be sufficient in many cases, but IQ-Firewatch and similar systems directly compete with ground level technologies such as sensor nodes, which may detect fires even before they emit visible amounts of smoke due to their sensors and proximity to the fire [82]. Furthermore, while camera systems are not directly constrained by the need to conserve power, they are tethered to the nearest power source, requiring users to consider optimal placements. By the same token, they benefit from stable power, eliminating problems with power sources such as batteries, allowing them to utilize more power-intensive tasks such as real-time data processing. Although each designer's techniques are trade secrets and often undisclosed, popular methods often involve smoke detection and machine learning, including the usual feature extraction from raw captures and applying various classifiers. [PERSON] et al., have reviewed such algorithms and listed their common extracted features: color, motion, texture, shape and energy [76].