Statistical reports point to the fact that civil infrastructure projects remain hazardous working environments. Despite the implementation of various safety procedures, the frequency and cost of work-related injuries are significant. Improvements in sensor technologies, wireless communication and processing power of computers as well as advancements in machine learning and computer vision are now enabling data-driven systems as effective safety barriers for accident prevention. In recent years, many researchers have studied various methods of leveraging technology to improve safety in civil infrastructure projects. However, previous investigations have not produced a thorough analysis of the practicality of those approaches. While considerable progress has been made in developing methods to improve construction safety, few studies have focused on implementation of data-driven real-time accident prevention systems to effectively minimize risk in the event where other safety measures have failed or been absent. Motivated to facilitate the development of such method, this paper carries out thorough analysis of the field and its trends, identifies research gaps, provides a discussion of recent advancements, and highlights future research directions to help researchers gain an up-to-date overview of the state-of-the-art and navigate through this domain efficiently.