Multi-class Obstacle Identification using Shape Descriptors in Video for Autonomous Vehicles

DOI

10.2991/icmia-17.2017.135How to use a DOI?

Keywords

Obstacle identification, Shape descriptor, Shape context, Axis of Least Inertia

Abstract

Obstacle identification has been widely studied as part of the broader obstacle detection research area for Autonomous Vehicles (AV). Existing in-vehicle sensing systems are concentrated on obstacle detection for pedestrian or vehicle, and limited work has been conducted on multi-class obstacle classification. In the process of obstacle identification, the selection of classification features is particularly critical. As a set of features to describe a given shape or contour, shape descriptor has attracted much attention in recent years and play an important role in pattern recognition. This paper proposed a shape descriptor based multi-class obstacle identification method where the traffic obstacles (in the front of self-vehicle) be classified into one of four classes: vehicle, lateral moving pedestrian, longitudinal moving pedestrian, and unknown (such as trees, road lamp, barricade etc.). Here a variety of shape descriptors extracted from the contour curve are involved, such as Rectangularity, Compactness, Elongation, Circularity, Shape Context, and Axis of Least Inertia. Finally, the identification results using these descriptors are contrastive analyzed. Though a single shape descriptor does not achieve ideal identification results for traffic obstacle, but this will provide a new idea for multi-class obstacle identification using shape descriptor in video for AV.

Copyright

© 2017, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Shumin Liu
AU  - Xujuan Xu
AU  - Longyun Duan
PY  - 2017/06
DA  - 2017/06
TI  - Multi-class Obstacle Identification using Shape Descriptors in Video for Autonomous Vehicles
BT  - Proceedings of the 2017 6th International Conference on Measurement, Instrumentation and Automation (ICMIA 2017)
PB  - Atlantis Press
SP  - 748
EP  - 755
SN  - 1951-6851
UR  - https://doi.org/10.2991/icmia-17.2017.135
DO  - 10.2991/icmia-17.2017.135
ID  - Liu2017/06
ER  -