Road obstacles positional and dynamic features extraction combining object detection, stereo disparity maps and optical flow data

One of the most relevant tasks in an intelligent vehicle navigation system is the detection of obstacles. It is important that a visual perception system for navigation purposes identifies obstacles, and it is also important that this system can extract essential information that may influence the vehicle’s behavior, whether it will be generating an alert for a human driver or guide an autonomous vehicle in order to be able to make its driving decisions. In this paper we present an approach for the identification of obstacles and extraction of class, position, depth and motion information from these objects that employs data gained exclusively from passive vision. We use a convolutional neural network for the obstacles detection, optical flow for the analysis of movement of the detected obstacles, both in relation to the direction and in relation to the intensity of the movement, and also stereo vision for the analysis of distance of obstacles in relation to the vehicle. We performed our experiments on two different datasets, and the results obtained showed a good efficacy from the use of depth and motion patterns to assess the obstacles’ potential threat status.