Abstract

The stixel world is a simplification of the world in which obstacles are represented as vertical instances, called stixels, standing on a surface assumed to be planar. In this paper, previous approaches for stixel tracking are extended using a two-level scheme. In the first level, stixels are tracked by matching them between frames using a bipartite graph in which edges represent a matching cost function. Then, stixels are clustered into sets representing objects in the environment. These objects are matched based on the number of stixels paired inside them. Furthermore, a faster, but less accurate approach is proposed in which only the second level is used. Several configurations of our method are compared to an existing state-of-the-art approach to show how our methodology outperforms it in several areas, including an improvement in the quality of the depth reconstruction.

Highlights

  • Considerable work has been carried out to improve the efficiency and performance of obstacle-detection methods applied to Advanced Driver Assistance Systems (ADAS)

  • Stixel depth accuracy compared to object-level tracking

  • We present an innovative object tracking method based on the stixel world [1]

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Summary

Introduction

Considerable work has been carried out to improve the efficiency and performance of obstacle-detection methods applied to Advanced Driver Assistance Systems (ADAS). The model only focuses on the dominant objects in the scene, without a pixel-wise depth map, meaning the model can be estimated much faster than with traditional tracking methods. To this end, Badino et al [1] proposed a representation of the world based on a set of rectangular sticks called stixels (from stick and pixel). This compact, but flexible representation of the world can be used as the common basis for scene understanding tasks. The stixels can be generated without calculating a depth map by using techniques, such as

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