Abstract
In this paper, an algorithm that recognizes wand signals for traffic control is proposed. The Society of Automotive Engineers (SAE) defines 6 levels of driving automation ranging from 0 (fully manual) to 5 (fully autonomous). Starting from the level 4, the driver does not operate the driving systems. At this level of automation, the artificial intelligence needs to take control even in the most unexpected situations such as malfunctioning traffic signals, driveway impaired by car accidents, or passing through construction sites. In such situations, police or traffic safety foreman may take control of the site and command the autonomous vehicles to slow down, stop or go. This paper presents a computer vision-based algorithm to detect and recognize the traffic control wand signals. The algorithm is composed of tracking the foreman (officer), detecting the wand, and classifying the action of the foreman based on wand trajectory by using Recurrent Neural Net(RNN) based networks.
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