Derin Öğrenme Tabanlı Vücut Bölme ve Gaussian Filtreleme Kullanarak Gözetim Videolarında Kişiyi Yeniden Tanıma

Bu makalede, daha önce bir kamera ağı üzerinden gözlemlenen bir kişiyi aramak için Kişiyi Yeniden Tanıma (Person ReIdentification) sistemi üzerine yoğunlaşıyoruz. Kişiyi Yeniden Tanıma önemli bir iştir. Örnek olarak kayıp veya şüpheli bir kişinin görüntüleri bulunuyorsa, Kişiyi Yeniden Tanıma sistemi, kişiyi video görüntülerinden bulunmasını sağlayabilir. Bu alanda kişinin yeniden tanımlanmasına ilişkin birçok araştırma olmasına rağmen, bu hala zor bir problem olmaya devam etmektedir ve birçok araştırma bu alanda devam etmektedir. Bu sorunu çözmek amacıyla, makalemizde derin öğrenme tabanlı insan vücudu bölümü bölümleme ve Gaussian filtreleme tabanlı pürüzsüz maske üretimi kullanarak Kişiyi Yeniden Tanıma Sistemi sunuyoruz. İnsan vücudu parçalarını bölümlere ayırmak ve yerel ikili maskeler oluşturmak için anlamsal bir bölme tekniği kullanıyoruz. Bu maskeler deterministik ikili görüntülerdir. Bu ikili maskelerin katı sınırları vardır ve bu deterministik maskelerle bazı özelliklerin kaybedilmesine sebep verip, performansı düşürmektedir. Bu nedenle, maskeleri pürüzsüz hale getirebilmek için Gaussian filtresi uyguluyoruz, böylece sınırlara yakın özellikler de performansa biraz katkı sağlıyor. Bizim geliştirdiğimiz metodumuzda, bu pürüzsüz maskeler, derin öğrenme ağının başında veya ortasında maske uygulayan diğer yöntemlerin aksine, ağın sonunda oluşturulan son özellik haritalarına uygulanmaktadır. Bu nedenle, işimiz yeni ve diğer çalışmalardan farklıdır çünkü ağın sonunda anlamsal bölümleme ve maskeleme kullanmanın yanı sıra bölümleme aşamasında hataları gidermek için maskemiz Gaussian filtresi ile pürüzsüz hale getirilmiştir. Global özellikleri çıkarmak için iyi bilinen önceden eğitilmiş bir ağ olan ResNet-50'yi ve insan vücudunun bölümlenmesi için Alanlar Arası Tamamlayıcı Öğrenme adlı bir yöntemi kullanıyoruz. Resnet-50 ağının sonunda çıkarılan global özelliklere Gaussian filtreli pürüzsüz yerel maskelerin uygulanması, kişiyi yeniden tanıma sisteminin performansını artıryor. Değerlendirme, yaygın olarak kabul edilen Market-1501 veri kümesi üzerinde gerçekleştirilmiştir ve sonuçlar umut vericidir.

Person Re-Identification in Surveillance Videos using Deep Learning based Body Part Partition and Gaussian Filtering

In this paper, we concentrate on Person Re-Identification (Re-ID) that consists of searching for a person who has been previouslyobserved over a camera network. Person Re-ID is important for searching suspicious or missing persons if we have sample images ofthe person of interest. Despite the fact that there are many researches on vision-based Person re-identification, it still remains achallenging problem. We propose a person re-identification system using a deep learning based human body part segmentation, andGaussian filtering based smooth mask generation. A semantic partition technique is used to segment human body parts and generatelocal binary masks. These masks are deterministic binary images. These binary masks have strict boundaries, and we lose somefeatures with these deterministic masks. Therefore, we apply Gaussian filter for smoothing masks so that features near the boundariesare also taken into account slightly. These smooth masks are applied to the final feature maps generated at the end of network oncontrary to other methods which apply mask at the beginning or in the middle of the deep learning network. Therefore, our work isnew and different from other works because of using semantic partition and masking at the end of network, as well as our mask aresmoothed with Gaussian filter to handle errors during the partitioning stage. We use a well-known pre-trained network, namelyResNet-50, to extract global features, and a method called Cross-Domain Complementary Learning for human body partitioning.Applying Gaussian filtered smooth local masks to the global features, which are extracted at the end of Resnet-50 network, increasesthe performance of Person Re-Identification system. Evaluation is conducted on a commonly accepted Market-1501 dataset, andresults are promising.

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