Method

Mixture of Bag-of-Words [la] [mBoW]


Submitted on 21 Mar. 2013 18:35 by
Jens Behley (University of Bonn)

Running time:10 s
Environment:1 core @ 2.5 Ghz (C/C++)

Method Description:
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.
Parameters:
see paper.
Latex Bibtex:
@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}}

Detailed Results

Object detection and orientation estimation results. Results for object detection are given in terms of average precision (AP) and results for joint object detection and orientation estimation are provided in terms of average orientation similarity (AOS).


Benchmark Easy Moderate Hard
Car (Detection) 37.63 % 23.76 % 18.44 %
Pedestrian (Detection) 44.36 % 31.37 % 30.62 %
Cyclist (Detection) 28.19 % 21.62 % 20.93 %
This table as LaTeX


2D object detection results.
This figure as: png eps pdf txt gnuplot



2D object detection results.
This figure as: png eps pdf txt gnuplot



2D object detection results.
This figure as: png eps pdf txt gnuplot




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