Foto-kapan Görüntülerinde Hareketli Nesne Tespiti ve Konumunun Belirlenmesi

Foto-kapanlar genellikle ormanlık arazide sabit noktaya yerleştirilmiş ve doğal yaşamı izlemek için kullanılan görüntüleme cihazlarıdır. Foto-kapanlar kullanılarak canlıların doğal yaşamı üzerinde araştırma yapmak amacıyla milyonlarca görüntü kaydedilmektedir. Kaydedilmiş görüntüler üzerinde bilgisayar tabanlı yöntemler ile canlıların tespit edilmesi ve tanınması amacıyla otomatik yöntemler geliştirilmektedir. Ayrıca foto-kapan görüntülerinde arka plan karmaşıklığı, arka planın hareketli olması, ışık şiddeti değişimi ve nesnenin parçalı olması gibi problemler hareketli nesne tespitini zorlaştırmaktadır. Literatürde bu amaçla yapılan çalışmalarda hareketli nesnelere ait model görüntüler görüntü içerisinden el ile tespit edilerek sınıflandırma tabanlı yöntemlerde ön bilgi olarak kullanılmaktadır. Nesnelere ait model görüntülerin el ile tespit edilmesi ve kırpılması zor, zahmetli, zaman alan bir süreçtir ve yüksek iş yükü gerektirmektedir. Çalışmamızda bu iş yükünü azaltmak amacıyla doğal ortamdan elde edilmiş foto-kapan görüntülerinde nesnelere ait ön bilgi kullanılmadan hareketli nesneler otomatik tespit edilmiş ve hareketli nesnelerin görüntüdeki konumları belirlenmiştir. Önerilen yöntemde hareketli nesnelerin tespit edilmesi için görüntülere arka plan çıkarma ve çerçeve farkı yöntemleri uygulanmıştır. Arka plan modelinin oluşturulması için Değişen Gauss Ortalama ve Gaussların Karışımı, gürültülerin azaltılması ve nesnelerin belirginleştirilmesi amacıyla Gauss Bulanıklığı ve Medyan filtre, ön plan tespitindeki hataların giderilmesi için OTSU eşikleme kullanılmıştır. Foto-kapan veri setlerinde hareketli nesne tespit etme başarısı %83, nesne konumlandırma başarısı ise %80 olarak elde edilmiştir.

Anahtar Kelimeler:

Hareketli Nesne Tespiti, Değişen Gauss Ortalama, Çerçeve Farkı, Fotokapan

Moving Object Detection and Localization in Camera-Trap Images

Camera-traps are usually placed on a fixed point in a forest land and are used to monitor natural life. Millions of images are recorded to investigate the natural life of living things by using camera-traps. Computer based automatic methods are developed for detecting and identifying living things on recorded images. Also problems such as background complexity, moving background, change of light intensity and fragmentations of the object in camera-trap images make moving object detection difficult. In the literature, for this purpose the model images of moving objects obtained from manually are used as preliminary information in classification based methods. Detecting and cropping model images of the objects manually is a difficult, laborious, time-consuming process and requires high workload. In our study, To reduce this workload it was aimed to detect moving objects automatically and to determine the location of moving objects in camera-trap images that obtained from natural environment. In the proposed method for this purpose, background extraction and frame difference methods were applied to the images. Gaussian Average and Mixture of Gaussian were used to create a background model. Gaussian Blur and Median Filter were used to reduce noise and to clarify objects. .OTSU thresholding was used to eliminate the errors of foregrounds. In the camera-trap data sets, the success of detecting moving objects was 82% and the object localization success was 80%.

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