A DEEPGAIT FEATURE EXTRACTION VIA MAXIMUM ACTIVATED CHANNEL LOCALIZATION AND ANALYTICAL STUDY ON MULTI-VIEW LARGE POPULATION GAIT DATASETS / (Kayıt no. 283413)
[ düz görünüm ]
| 000 -BAŞLIK | |
|---|---|
| Sabit Uzunluktaki Kontrol Alanı | 03050nam a22003137a 4500 |
| 003 - KONTROL NUMARASI KİMLİĞİ | |
| Kontrol Alanı | KOHA_MİRAKIL |
| 005 - EN SON İŞLEM TARİHİ ve ZAMANI | |
| Kontrol Alanı | 20211203112827.0 |
| 008 - SABİT UZUNLUKTAKİ VERİ ÖGELERİ - GENEL BİLGİ | |
| Sabit Alan | 211203d2021 cy ||||| m||| 00| 0 eng d |
| 040 ## - KATALOGLAMA KAYNAĞI | |
| Özgün Kataloglama Kurumu | CY-NiCIU |
| Kataloglama Dili | eng |
| Çeviri Kurumu | CY-NiCIU |
| Açıklama Kuralları | rda |
| 041 ## - DİL KODU | |
| Metin ya da ses kaydının dil kodu | eng |
| 090 ## - Yerel Tasnif No | |
| tasnif no | D 267 |
| Cutter no | M94 2021 |
| 100 1# - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Muhammed, Salisu |
| 245 12 - ESER ADI BİLDİRİMİ | |
| Başlık | A DEEPGAIT FEATURE EXTRACTION VIA MAXIMUM ACTIVATED CHANNEL LOCALIZATION AND ANALYTICAL STUDY ON MULTI-VIEW LARGE POPULATION GAIT DATASETS / |
| Sorumluluk Bildirimi | SALISU MUHAMMED; SUPERVISOR: ASSOC. PROF. DR. ERBUĞ ÇELEBI |
| 246 23 - DEĞİŞİK BAŞLIK FORMU | |
| Başlık uygun / kısa başlık | A STUDY OF PUBLIC SECTORS IN ERBIL |
| 264 ## - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | 2021 |
| 300 ## - FİZİKSEL TANIMLAMA | |
| Sayfa, Cilt vb. | 103 sheets; |
| Boyutları | 31 cm. |
| Birlikteki Materyal | Includes CD |
| 336 ## - CONTENT TYPE | |
| Source | rdacontent |
| Content type term | text |
| Content type code | txt |
| 337 ## - MEDIA TYPE | |
| Source | rdamedia |
| Media type term | unmediated |
| Media type code | n |
| 338 ## - CARRIER TYPE | |
| Source | rdacarrier |
| Carrier type term | volume |
| Carrier type code | nc |
| 502 ## - TEZ NOTU | |
| Tez Notu | Thesis (PhD) - Cyprus International University. Institute of Graduate Studies and Research Computer Engineering Department |
| 504 ## - BİBLİYOGRAFİ NOTU | |
| Bibliyografi Notu | Includes bibliography (sheets 99-103) |
| 520 ## - ÖZET NOTU | |
| Özet notu | ABSTRACT<br/>In this study, a novel maximum activated channel localization framework was created for extracting DeepGait features. In addition, as the models with fewer operations help realize the performance of intelligent computing systems, a Channel-Activated Mapping Network (CAMNet) for DeepGait feature extraction with less operation without dimension decomposition was proposed. More explicitly, the CAMNet is composed of an improved GEINet (three progressive triplets of convolution, batch normalization, and ReLu layers and then two internal max-pooling layers), an external max-pooling to capture the Spatio-temporal information of multiple frames in one gait period. We conducted experiments to validate the effectiveness of the proposed novel algorithm in terms of cross-view gait recognition in both cooperative and uncooperative settings using the state-of-the-art Osaka University Multi-View Large Population OU-MVLP dataset. The OU-MVLP dataset includes 10,307 subjects. As a result, we confirmed that the CAMNet+KNN significantly outperformed state-of-the-art approaches using the same dataset at the rear angles of 180, 195, 210, and 225, in both cooperative and uncooperative settings. The study also gives a comprehensive insight into the natural adversaries found in a multi-view large population dataset. Based on the analyses carried out on the OU-MVLP dataset, we have found that capturing gait frames at view angles 45o gives an equal number of frames in multiple sequences. However, 30o is the second view angle that also gives an equal number of frames in multiple sequences. In terms of age groups, 9-12 is the group that was found to have a higher percentage of subjects with an equal number of frames among the two sequences. |
| 650 #0 - KONU BAŞLIĞI EK GİRİŞ - KONU TERİMİ | |
| Konusal terim veya coğrafi ad | Data sets |
| Alt başlık biçimi | Dissertations, Academic |
| 650 #0 - KONU BAŞLIĞI EK GİRİŞ - KONU TERİMİ | |
| Konusal terim veya coğrafi ad | Computer algorithms |
| Alt başlık biçimi | Dissertations, Academic |
| 650 #0 - KONU BAŞLIĞI EK GİRİŞ - KONU TERİMİ | |
| Konusal terim veya coğrafi ad | Computer engineering |
| Alt başlık biçimi | Dissertations, Academic |
| 700 1# - EK GİRİŞ - KİŞİ ADI | |
| Yazar Adı (Kişi adı) | Çelebi, Erbuğ |
| İlişkili Terim | supervisor |
| 9 (RLIN) | 1665 |
| 942 ## - EK GİRİŞ ÖGELERİ (KOHA) | |
| Sınıflama Kaynağı | Dewey Onlu Sınıflama Sistemi |
| Materyal Türü | Thesis |
| Geri Çekilme Durumu | Kayıp Durumu | Sınıflandırma Kaynağı | Kredi için değil | Koleksiyon Kodu | Kalıcı Konum | Mevcut Konum | Raf Yeri | Kayıt Tarih | Source of acquisition | Yer Numarası | Demirbaş Numarası | Son Görülme Tarihi | Fatura Tarihi | Materyal Türü | Genel / Bağış Notu |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Dewey Onlu Sınıflama Sistemi | Tez Koleksiyonu | CIU LIBRARY | CIU LIBRARY | Tez Koleksiyonu | 03.12.2021 | Bağış | D 267 M94 2021 | T2535 | 03.12.2021 | 03.12.2021 | Thesis | Computer Engineering Department | |||
| Dewey Onlu Sınıflama Sistemi | CIU LIBRARY | CIU LIBRARY | Görsel İşitsel | 03.12.2021 | Bağış | D 267 M94 2021 | CDT2535 | 03.12.2021 | 03.12.2021 | Suppl. CD | Computer Engineering Department |