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LiDAR Pedestrian Detector and Semi-Automatic Annotation Tool for Labeling of 3D Data

Andersson, Roy LU and Andersson, Erik LU (2019) In Master's Theses in Mathematical Sciences FMAM05 20191
Mathematics (Faculty of Engineering)
Abstract
The goal of this Master's Thesis is to successfully detect and classify humans in a LiDAR data stream. The focus of the Thesis is on the detection and classification, not on the LiDAR technology.

To classify humans machine learning was used and to train the machine learning model we collected our own data and annotated it. A custom software was made for speeding up the annotation process.

The process for detecting humans in a scene was to first sweep the scene with a fixed size box which contain a point cloud. These point clouds were then split up by a clustering algorithm. Finally features were extracted from the clusters and classified using a classification algorithm.

The algorithm of choice for prediction became the Random... (More)
The goal of this Master's Thesis is to successfully detect and classify humans in a LiDAR data stream. The focus of the Thesis is on the detection and classification, not on the LiDAR technology.

To classify humans machine learning was used and to train the machine learning model we collected our own data and annotated it. A custom software was made for speeding up the annotation process.

The process for detecting humans in a scene was to first sweep the scene with a fixed size box which contain a point cloud. These point clouds were then split up by a clustering algorithm. Finally features were extracted from the clusters and classified using a classification algorithm.

The algorithm of choice for prediction became the Random Forest classifier which successfully classified unobstructed humans in different environments but occasionally gave false positives. (Less)
Popular Abstract
LiDAR has been a fast growing technology recent years, mainly because of its use in autonomous vehicles and for mapping of terrains. In this project a pedestrian detector was created using a LiDAR and machine learning.
Please use this url to cite or link to this publication:
author
Andersson, Roy LU and Andersson, Erik LU
supervisor
organization
course
FMAM05 20191
year
type
H2 - Master's Degree (Two Years)
subject
keywords
LiDAR, Machine Learning, Pedestrian Detection, Annotation, DBSCAN, SVM
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMA-3390-2019
ISSN
1404-6342
other publication id
2019:E54
language
English
additional info
Last edited 27/08/2019
id
8993073
date added to LUP
2019-09-05 14:00:42
date last changed
2019-09-05 14:00:42
@misc{8993073,
  abstract     = {{The goal of this Master's Thesis is to successfully detect and classify humans in a LiDAR data stream. The focus of the Thesis is on the detection and classification, not on the LiDAR technology. 

To classify humans machine learning was used and to train the machine learning model we collected our own data and annotated it. A custom software was made for speeding up the annotation process. 

The process for detecting humans in a scene was to first sweep the scene with a fixed size box which contain a point cloud. These point clouds were then split up by a clustering algorithm. Finally features were extracted from the clusters and classified using a classification algorithm.

The algorithm of choice for prediction became the Random Forest classifier which successfully classified unobstructed humans in different environments but occasionally gave false positives.}},
  author       = {{Andersson, Roy and Andersson, Erik}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{LiDAR Pedestrian Detector and Semi-Automatic Annotation Tool for Labeling of 3D Data}},
  year         = {{2019}},
}