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

TL;DR

This paper presents a vision-based method for detecting obstacles and extracting their class, position, depth, and motion features to improve autonomous vehicle navigation, using stereo disparity and optical flow data.

Contribution

It introduces a novel approach combining object detection, stereo disparity, and optical flow for obstacle characterization in passive vision systems.

Findings

Effective obstacle threat assessment using depth and motion patterns.

Good performance demonstrated on two different datasets.

Enhanced obstacle feature extraction for navigation decisions.

Abstract

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 performed our experiments on two different data-sets and the results obtained shown a good efficacy from the use of depth and motion patterns to assess the obstacles' potential threat status.