The KITTI Vision Benchmark Suite

A segment-based object detection approach using
laser range data only. Our detection approach is
built up of three stages: First, a hierarchical
segmentation approach generates a hierarchy of
coarse-to-fine segments to reduce the impact of
over- and under-segmentation in later stages. Next,
we employ a learned distance-dependent mixture
model to classify all segments. The model combines
multiple softmax regression classifiers learned on
specific bag-of-word representations using
different parameterizations of a descriptor. In the
final stage, we filter irrelevant and duplicate
detections using a greedy method in consideration
of the segment hierarchy.

Originally submitted by Jens Behley (University of
Bonn).

See paper.

@inproceedings{Behley2013IROS,
author = {Jens Behley and Volker Steinhage and
Armin B. Cremers},
title = {{Laser-based Segment Classification Using
a Mixture of Bag-of-Words}},
booktitle = {Proc. of the IEEE/RSJ International
Conference on Intelligent Robots and Systems
(IROS)},
year = {2013}}