Automatic target recognition performance losses in the presence of atmospheric and camera effects

Abstract

The importance of networked automatic target recognition systems for surveillance applications is continuously increasing. Because of the requirement of a low cost and limited payload, these networks are traditionally equipped with lightweight, low-cost sensors such as electro-optical (EO) or infrared sensors. The quality of imagery acquired by these sensors critically depends on the environmental conditions, type and characteristics of sensors, and absence of occluding or concealing objects. In the past, a large number of efficient detection, tracking, and recognition algorithms have been designed to operate on imagery of good quality. However, detection and recognition limits under nonideal environmental and/or sensor-based distortions have not been carefully evaluated. We introduce a fully automatic target recognition system that involves a Haar-based detector to select potential regions of interest within images, performs adjustment of detected regions, segments potential targets using a region-based approach, identifies targets using Bessel K form-based encoding, and performs clutter rejection. We investigate the effects of environmental and camera conditions on target detection and recognition performance. Two databases are involved. One is a simulated database generated using a 3-D tool. The other database is formed by imaging 10 die-cast models of military vehicles from different elevation and orientation angles. The database contains imagery acquired both indoors and outdoors. The indoors data set is composed of clear and distorted images. The distortions include defocus blur, sided illumination, low contrast, shadows, and occlusions. All images in this database, however, have a uniform (blue) background. The indoors database is applied to evaluate the degradations of recognition performance due to camera and illumination effects. The database collected outdoors includes a real background and is much more complex to process. The numerical results demonstrate that the complexity of the background and the presence of occlusions present a serious challenge for automatic target detection and recognition.

GPU-based infrared sensor model capable of operating in real time

“…Imgede KÖ görüntüleme sensörüne baglı olarak ortaya çıkan bozulmalar (bulanma ve gürültü), hedef tespit algoritmalarının başarımını önemli oranda etkileyebilmektedir [1]. Özellikle, atmosferik etkilerin baskın oldugu durumlar altında imge bozulmalarına baglı olarak hedefin uzak mesafelerden tespit edilebilme olasılıgı önemli ölçülerde azalmaktadır [2].…”

Evaluation of a target recognition algorithm using the sensor model

“…Sentetik imgelerden faydalanılarak atmosferin, hareketin ve sensörözelliklerinin hedef tespit, takip ve tanıma algoritmalarının başarımlarıüzerindeki etkilerinin incelenmesi kapsamında literatürde yapılmış olan detaylı çalışma sayısı oldukça azdır [3]. Referans [3]'de, bu faktörlerin otomatik hedef tanıma algoritmalarının başarımlarına olan etkileri sentetik görüntüler uzerinden incelenmiş, ancak türbülans ve aerosol gibi atmosferik faktörlerin sebep oldugu etkiler detaylandırılmamıştır.…”

“…Referans [3]'de, bu faktörlerin otomatik hedef tanıma algoritmalarının başarımlarına olan etkileri sentetik görüntüler uzerinden incelenmiş, ancak türbülans ve aerosol gibi atmosferik faktörlerin sebep oldugu etkiler detaylandırılmamıştır.…”