You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Aiming to reduce pollutant emissions, bicycles are regaining popularity specially in urban areas. However, the number of cyclists' fatalities is not showing the same decreasing trend as the other traffic groups. Hence, monitoring cyclists' data appears as a keystone to foster urban cyclists' safety by helping urban planners to design safer cyclist routes. In this work, we propose a fully image-based framework to assess the rout risk from the cyclist perspective. From smartphone sequences of images, this generic framework is able to automatically identify events considering different risk criteria based on the cyclist's motion and object detection. Moreover, since it is entirely based on images, our method provides context on the situation and is independent from the expertise level of the cyclist. Additionally, we build on an existing platform and introduce several improvements on its mobile app to acquire smartphone sensor data, including video. From the inertial sensor data, we automatically detect the route segments performed by bicycle, applying behavior analysis techniques. We test our methods on real data, attaining very promising results in terms of risk classification, according to two different criteria, and behavior analysis accuracy.

Cited Abstract(s):
[1]: One of the main challenges in the conduct of detailed analysis of cyclist behavior is the lack of reliable data. Collection of data through manual methods is a labor-intensive and time-consuming process. Two of the important areas of cyclist data collection are volume counts and average speed measurements. A volume count provides the basis for necessary exposure measures and conveys essential information about traffic patterns. Cyclist speed data are used for traffic control and safety studies. The application of computer vision (CV) techniques enables the collection of precise spatial and temporal measurements of road users in a resource-efficient way. This paper presents the use of a set of CV techniques for the automated collection of cyclist data. Cyclist tracks obtained from video analysis were used to perform screen line counts as well as cyclist speed measurements. The applications were demonstrated with the use of a real-world data set from a roundabout in Vancouver, British Columbia, Canada. Further analysis was conducted on the mean speed of cyclists with regard to several factors (e.g., travel path, helmet use, group size). The motivation for this research was to understand better cyclist behavior and how it varied under different conditions. Several conclusions could be drawn from the analysis of cyclist speed behavior. Group size, travel path, lane position, and helmet use were all found to affect the cyclist mean speed. Single cyclists had a slightly, but significantly, higher mean cycling speed than did group cyclists. The mean cycling speed was highest for those cyclists who used the road rather than the sidewalk. The mean cycling speed decreased for cyclists without helmets.

[2]: Abstract Camera based systems are routinely used for monitoring highway traffic, supplementing inductive loops and microwave sensors employed for counting purposes. These techniques achieve very good counting accuracy and are capable of discriminating trucks and cars. However, pedestrians and cyclists are mostly counted manually. In this paper, we describe a new camera based automatic system that utilizes Kalman filtering in tracking and Learning Vector Quantization for classifying the observations to pedestrians and cyclists. Both the requirements for such systems and the algorithms used are described. The tests performed show that the system achieves around 80–90 accuracy in counting and classification.
Here we follow a different direction, taking advantage of the good properties yielded by using images as primary source of data. Indeed, computer vision techniques have been applied, for quite some time, to traditional cyclist monitoring tasks as volume counts [2] and average speed, due to their reliability and efficiency when compared to manual methods [1] . However, so far no work has addressed identification of dangerous situations using an on-board smartphone camera.