Abstract:
In a world moving more towards green initiatives, cycling has become a major way of
transportation for many people across the world. Due to the rapid growth of automobile usage,
cyclists are considered as one of the most vulnerable groups of road users. Even though there are
various Collision avoiding systems available right now, most of them focus on pedestrians and
highway driving scenarios. There are a smaller number of systems that focus on the safety of
cyclists. Detecting Cyclists and predicting their intentions real-time may help in increasing cyclist
safety in an urban environment. Some existing research require ideal conditions to predict the
cyclist state while few are implemented exploiting various constants in the environment. Some
research work requires to have known jersey patterns to detect cyclists among other automobile
whereas some other work requires the data such as curb position and location to be
constant/predefined while the ethnicity of the demography is also considered while it predicts only
risky cycling scenarios. This research presents a holistic solution to detect and recognize cyclists
in a complex environment without specific user given information focusing on ellipses detection
applied to wheel patterns of the cyclists.
Citation:
Mahawatta, D.M.A. (2022). Cyclist state prediction system [Master's theses, University of Moratuwa]. Institutional Repository University of Moratuwa. http://dl.lib.uom.lk/handle/123/21881