Computer vision‐based recognition of driver distraction: A review

Vehicle crash rates caused by distracted driving have been rising in recent years. Hence, safety while driving on roads is today a crucial concern across the world. Some of the reasons due to which drivers may lose attention include the use of mobile phones, speaking with passengers, and reaching behind to grab something while driving. There are various types of distractions, out of which we focus on manual ones based on the posture of the driver. This work presents a review on open problems and challenges concerning the vision‐based distraction recognition of drivers. Types of experimental environments are described; and image modalities, public and custom datasets and body parts inspected during distraction recognition are explored. Besides, a comparative review of different works on vision‐based recognition of driver's manual distractions and limitations for each dataset is presented. Main approaches of vision‐based manual distractive driving recognition can be categorized into conventional and deep learning methods. These approaches are compared and classified based on whether using temporal information or not. Finally, we give some suggestions for improvement, and look forward to future development directions of the vision‐based recognition of driver's manual distractions.