## 2 Literature Review
### IoT Technologies for Roadside Surveillance
Due to the instability of communication and concerns about interruption, WSNs may not be appropriate for providing road users and the public with accurate traffic data and information. Furthermore, technological improvement has led to the development of semi-automatic systems (known as computer vision) for creating algorithms by using computers for automatic real-time object and event detection and to aid recognition [24]. According to [PERSON] [25], from an engineering perspective, computer vision aims at building autonomous systems and seeks to automate, with greater efficiency, tasks that are performed by the human visual system. In fact, several monitoring objectives can be supported by the application of computer vision and pattern recognition techniques, such as detection of traffic violations and identification of road users [26]. [PERSON] et al. [27] presented a feature-based tracking approach for the task of tracking vehicles during periods of traffic congestion. Video cameras were expected to be mounted on poles or other tall structures looking down at the traffic scene. [PERSON] and [PERSON] [28] designed a real-time and colour-based computer vision system for traffic monitoring, by analysing colour image sequences of traffic scenes recorded by mounting a stationary camera on a tall building or a pedestrian crossing bridge near a traffic light. Although computer vision-based surveillance systems have various functionalities, such as vehicle detection, recognition and classification, the systems require relatively long processing times and considerable memory, since converting collected data into useful information can be challenging. In addition, the set-up for camera calibration to facilitate the extraction of the required images is difficult and time-consuming [29]. However, according to [PERSON] et al. [30], machine learning can be applied to enhance the accuracy and efficiency of detection and recognition of an object, using computer vision-based surveillance systems. Thus, systems are able to learn without being explicitly programmed, by exploring the construction of algorithms. This facilitates more efficient calculation and measurement of real-time information on roadside activities, occupancy, vacancy and traffic parameters. In summary, an effective roadside surveillance system is critical for maintaining road safety, alleviating traffic congestion and facilitating traffic and fleet management. Applications for the measurement of traffic parameters and car park management utilizing computer vision, show an increasing trend. Computer vision technology can be powerful when properly matched with machine learning and big data analysis, outweighing the disadvantage of long processing times. In addition, this can lead to better analysis, with a high level of accuracy and efficiency [31]. Furthermore, improvements in camera calibration should be considered when utilizing computer vision, to maintain the accuracy, efficiency and effectiveness of the systems and to account for various features on different roads, such as the surroundings and the amount of sunlight.