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Improving a real-time object detector with compact temporal information

Ahrnbom, Martin LU orcid ; Bornø Jensen, Morten ; Åström, Karl LU orcid ; Nilsson, Mikael LU ; Ardö, Håkan LU and Moeslund, Thomas (2018) 16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017 p.190-197
Abstract
Neural networks designed for real-time object detection
have recently improved significantly, but in practice, look-
ing at only a single RGB image at the time may not be ideal.
For example, when detecting objects in videos, a foreground
detection algorithm can be used to obtain compact temporal
data, which can be fed into a neural network alongside RGB
images. We propose an approach for doing this, based on
an existing object detector, that re-uses pretrained weights
for the processing of RGB images. The neural network was
tested on the VIRAT dataset with annotations for object de-
tection, a problem this approach is well suited for. The ac-
curacy was found to improve significantly (up to 66%),... (More)
Neural networks designed for real-time object detection
have recently improved significantly, but in practice, look-
ing at only a single RGB image at the time may not be ideal.
For example, when detecting objects in videos, a foreground
detection algorithm can be used to obtain compact temporal
data, which can be fed into a neural network alongside RGB
images. We propose an approach for doing this, based on
an existing object detector, that re-uses pretrained weights
for the processing of RGB images. The neural network was
tested on the VIRAT dataset with annotations for object de-
tection, a problem this approach is well suited for. The ac-
curacy was found to improve significantly (up to 66%), with
a roughly 40% increase in computational time. (Less)
Please use this url to cite or link to this publication:
author
; ; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
host publication
International Conference on Computer Vision Workshops, 2017 : Computer Vision for Road Scene Understanding and Autonomous Driving Workshop - Computer Vision for Road Scene Understanding and Autonomous Driving Workshop
pages
8 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
16th IEEE International Conference on Computer Vision Workshops, ICCVW 2017
conference location
Venice, Italy
conference dates
2017-10-22 - 2017-10-29
external identifiers
  • scopus:85046282569
DOI
10.1109/ICCVW.2017.31
project
Semantic Mapping and Visual Navigation for Smart Robots
In-Depth understanding of accident causation for Vulnerable road users
Lund University AI Research
language
English
LU publication?
yes
id
41030db4-c01e-4609-be48-63157ea594d4
alternative location
http://openaccess.thecvf.com/content_ICCV_2017_workshops/papers/w3/Ahrnbom_Improving_a_Real-Time_ICCV_2017_paper.pdf
date added to LUP
2018-01-27 08:51:36
date last changed
2022-04-25 05:22:49
@inproceedings{41030db4-c01e-4609-be48-63157ea594d4,
  abstract     = {{Neural networks designed for real-time object detection<br/>have recently improved significantly, but in practice, look-<br/>ing at only a single RGB image at the time may not be ideal.<br/>For example, when detecting objects in videos, a foreground<br/>detection algorithm can be used to obtain compact temporal<br/>data, which can be fed into a neural network alongside RGB<br/>images. We propose an approach for doing this, based on<br/>an existing object detector, that re-uses pretrained weights<br/>for the processing of RGB images. The neural network was<br/>tested on the VIRAT dataset with annotations for object de-<br/>tection, a problem this approach is well suited for. The ac-<br/>curacy was found to improve significantly (up to 66%), with<br/>a roughly 40% increase in computational time.}},
  author       = {{Ahrnbom, Martin and Bornø Jensen, Morten and Åström, Karl and Nilsson, Mikael and Ardö, Håkan and Moeslund, Thomas}},
  booktitle    = {{International Conference on Computer Vision Workshops, 2017 : Computer Vision for Road Scene Understanding and Autonomous Driving Workshop}},
  language     = {{eng}},
  month        = {{01}},
  pages        = {{190--197}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Improving a real-time object detector with compact temporal information}},
  url          = {{http://dx.doi.org/10.1109/ICCVW.2017.31}},
  doi          = {{10.1109/ICCVW.2017.31}},
  year         = {{2018}},
}