Publicacions CVC
Home
|
Show All
|
Simple Search
|
Advanced Search
|
Add Record
|
Import
You must login to submit this form!
Login
Quick Search:
Field:
main fields
author
title
publication
keywords
abstract
created_date
call_number
contains:
...
Edit the following record:
Author
...
is Editor
Title
...
Type
Journal Article
Abstract
Book Chapter
Book Whole
Conference Article
Conference Volume
Journal
Magazine Article
Manual
Manuscript
Map
Miscellaneous
Newspaper Article
Patent
Report
Software
Year
...
Publication
...
Abbreviated Journal
...
Volume
...
Issue
...
Pages
...
Keywords
...
Abstract
Pedestrian classifiers decide which image windows contain a pedestrian. In practice, such classifiers provide a relatively high response at neighbor windows overlapping a pedestrian, while the responses around potential false positives are expected to be lower. An analogous reasoning applies for image sequences. If there is a pedestrian located within a frame, the same pedestrian is expected to appear close to the same location in neighbor frames. Therefore, such a location has chances of receiving high classification scores during several frames, while false positives are expected to be more spurious. In this paper we propose to exploit such correlations for improving the accuracy of base pedestrian classifiers. In particular, we propose to use two-stage classifiers which not only rely on the image descriptors required by the base classifiers but also on the response of such base classifiers in a given spatiotemporal neighborhood. More specifically, we train pedestrian classifiers using a stacked sequential learning (SSL) paradigm. We use a new pedestrian dataset we have acquired from a car to evaluate our proposal at different frame rates. We also test on a well known dataset: Caltech. The obtained results show that our SSL proposal boosts detection accuracy significantly with a minimal impact on the computational cost. Interestingly, SSL improves more the accuracy at the most dangerous situations, i.e. when a pedestrian is close to the camera.
Address
...
Corporate Author
...
Thesis
Bachelor's thesis
Master's thesis
Ph.D. thesis
Diploma thesis
Doctoral thesis
Habilitation thesis
Publisher
...
Place of Publication
...
Editor
...
Language
...
Summary Language
...
Original Title
...
Series Editor
...
Series Title
...
Abbreviated Series Title
...
Series Volume
...
Series Issue
...
Edition
...
ISSN
...
ISBN
...
Medium
...
Area
...
Expedition
...
Conference
...
Notes
...
Approved
yes
no
Location
Call Number
...
Serial
Marked
yes
no
Copy
true
fetch
ordered
false
Selected
yes
no
User Keys
...
User Notes
...
User File
...
User Groups
...
Cite Key
...
Related
...
File
URL
...
DOI
...
Online publication. Cite with this text:
...
Location Field:
don't touch
add
remove
my name & email address
Home
SQL Search
|
Library Search
|
Show Record
|
Extract Citations
Help