Improving a real-time object detector with compact temporal information
(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:
https://lup.lub.lu.se/record/41030db4-c01e-4609-be48-63157ea594d4
- author
- Ahrnbom, Martin LU ; Bornø Jensen, Morten ; Åström, Karl LU ; Nilsson, Mikael LU ; Ardö, Håkan LU and Moeslund, Thomas
- organization
- publishing date
- 2018-01-19
- 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}}, }