A Direct Approach for Human Detection With Catadioptric Omnidirectional Cameras

dc.contributor.author Çınaroğlu, İbrahim
dc.contributor.author Baştanlar, Yalın
dc.coverage.doi 10.1109/SIU.2014.6830719
dc.date.accessioned 2017-04-27T13:08:50Z
dc.date.available 2017-04-27T13:08:50Z
dc.date.issued 2014
dc.description 22nd Signal Processing and Communications Applications Conference, SIU 2014; Trabzon; Turkey; 23 April 2014 through 25 April 2014 en_US
dc.description.abstract This paper presents an omnidirectional vision based solution to detect human beings. We first go through the conventional sliding window approaches for human detection. Then, we describe how the feature extraction step of the conventional approaches should be modified for a theoretically correct and effective use in omnidirectional cameras. In this way we perform human detection directly on the omnidirectional images without converting them to panoramic or perspective image. Our experiments, both with synthetic and real images show that the proposed approach produces successful results. © 2014 IEEE. en_US
dc.identifier.citation Çınaroğlu, İ., and Baştanlar, Y. (2014, April 23-25). A Direct approach for human detection with catadioptric omnidirectional cameras. Paper presented at the 22nd Signal Processing and Communications Applications Conference. doi:10.1109/SIU.2014.6830719 en_US
dc.identifier.doi 10.1109/SIU.2014.6830719
dc.identifier.doi 10.1109/SIU.2014.6830719 en_US
dc.identifier.isbn 9781479948741
dc.identifier.scopus 2-s2.0-84903772827
dc.identifier.uri http://doi.org/10.1109/SIU.2014.6830719
dc.identifier.uri https://hdl.handle.net/11147/5427
dc.language.iso en en_US
dc.publisher Institute of Electrical and Electronics Engineers Inc. en_US
dc.relation.ispartof 22nd Signal Processing and Communications Applications Conference, SIU 2014 en_US
dc.rights info:eu-repo/semantics/openAccess en_US
dc.subject Video cameras en_US
dc.subject Human detection en_US
dc.subject Object detection en_US
dc.subject Omnidirectional cameras en_US
dc.subject Pedestrian detection en_US
dc.title A Direct Approach for Human Detection With Catadioptric Omnidirectional Cameras en_US
dc.type Conference Object en_US
dspace.entity.type Publication
gdc.author.institutional Çınaroğlu, İbrahim
gdc.author.institutional Baştanlar, Yalın
gdc.author.yokid 176747
gdc.bip.impulseclass C4
gdc.bip.influenceclass C4
gdc.bip.popularityclass C4
gdc.coar.access open access
gdc.coar.type text::conference output
gdc.collaboration.industrial false
gdc.description.department İzmir Institute of Technology. Computer Engineering en_US
gdc.description.endpage 2279 en_US
gdc.description.publicationcategory Konferans Öğesi - Uluslararası - Kurum Öğretim Elemanı en_US
gdc.description.scopusquality N/A
gdc.description.startpage 2275 en_US
gdc.description.wosquality N/A
gdc.identifier.openalex W2088205157
gdc.identifier.wos WOS:000356351400548
gdc.index.type WoS
gdc.index.type Scopus
gdc.oaire.diamondjournal false
gdc.oaire.impulse 11.0
gdc.oaire.influence 4.3472843E-9
gdc.oaire.isgreen true
gdc.oaire.keywords Object detection
gdc.oaire.keywords Omnidirectional cameras
gdc.oaire.keywords Human detection
gdc.oaire.keywords Pedestrian detection
gdc.oaire.keywords Video cameras
gdc.oaire.popularity 8.530689E-9
gdc.oaire.publicfunded false
gdc.oaire.sciencefields 0202 electrical engineering, electronic engineering, information engineering
gdc.oaire.sciencefields 02 engineering and technology
gdc.openalex.collaboration National
gdc.openalex.fwci 2.89369854
gdc.openalex.normalizedpercentile 0.93
gdc.openalex.toppercent TOP 10%
gdc.opencitations.count 18
gdc.plumx.crossrefcites 5
gdc.plumx.mendeley 8
gdc.plumx.scopuscites 33
gdc.scopus.citedcount 33
gdc.wos.citedcount 21
relation.isAuthorOfPublication.latestForDiscovery 7f75e80a-0468-490d-ba2e-498de80b7217
relation.isOrgUnitOfPublication.latestForDiscovery 9af2b05f-28ac-4014-8abe-a4dfe192da5e

Files

Original bundle

Now showing 1 - 1 of 1
Loading...
Name:
5427.pdf
Size:
937.43 KB
Format:
Adobe Portable Document Format
Description:
Conference Paper

License bundle

Now showing 1 - 1 of 1
Loading...
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description: