Automatic target recognition and detection in infrared imagery under cluttered background

Date

2017-09-14

Author

GÜNDOĞDU, ERHAN
KOÇ, AYKUT
Alatan, Abdullah Aydın

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

425
views

0
downloads

Visual object classification has long been studied in visible spectrum by utilizing conventional cameras. Since the labeled images has recently increased in number, it is possible to train deep Convolutional Neural Networks (CNN) with significant amount of parameters. As the infrared (IR) sensor technology has been improved during the last two decades, labeled images extracted from IR sensors have been started to be used for object detection and recognition tasks. We address the problem of infrared object recognition and detection by exploiting 15K images from the real-field with long-wave and mid-wave IR sensors. For feature learning, a stacked denoising autoencoder is trained in this IR dataset. To recognize the objects, the trained stacked denoising autoencoder is fine-tuned according to the binary classification loss of the target object. Once the training is completed, the test samples are propagated over the network, and the probability of the test sample belonging to a class is computed. Moreover, the trained classifier is utilized in a detect-by-classification method, where the classification is performed in a set of candidate object boxes and the maximum confidence score in a particular location is accepted as the score of the detected object. To decrease the computational complexity, the detection step at every frame is avoided by running an efficient correlation filter based tracker. The detection part is performed when the tracker confidence is below a pre-defined threshold. The experiments conducted on the real field images demonstrate that the proposed detection and tracking framework presents satisfactory results for detecting tanks under cluttered background.

Subject Keywords

Infrared object recognition, Neural networks, Autoencoders

URI

https://hdl.handle.net/11511/36040

DOI

https://doi.org/10.1117/12.2278869

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

3D object recognition from range images İzciler, Fatih; Halıcı, Uğur; Department of Electrical and Electronics Engineering (2012) Recognizing generic objects by single or multi view range images is a contemporary popular problem in 3D object recognition area with developing technology of scanning devices such as laser range scanners. This problem is vital to current and future vision systems performing shape based matching and classification of the objects in an arbitrary scene. Despite improvements on scanners, there are still imperfections on range scans such as holes or unconnected parts on images. This studyobjects at proposing an...
A multimodal approach for individual tracking of people and their belongings Beyan, Çiğdem; Temizel, Alptekin (2015-04-01) In this study, a fully automatic surveillance system for indoor environments which is capable of tracking multiple objects using both visible and thermal band images is proposed. These two modalities are fused to track people and the objects they carry separately using their heat signatures and the owners of the belongings are determined. Fusion of complementary information from different modalities (for example, thermal images are not affected by shadows and there is no thermal reflection or halo effect in...
New method for the fusion of complementary information from infrared and visual images for object detection Ulusoy, İlkay (Institution of Engineering and Technology (IET), 2011-02-01) Visual and infrared cameras have complementary properties and using them together may increase the performance of object detection applications. Although the fusion of visual and infrared information results in a better recall rate than using only one of those domains, there is always a decrease in the precision rate whereas the infrared domain on its own always has higher precision. Thus, the fusion of these domains is meaningful only for a better recall rate, which means that more foreground pixels are de...
Does estimated depth help object detection? Çetinkaya, Bedrettin; Akbaş, Emre; Department of Computer Engineering (2019) With the widespread use of RGB-D cameras, depth information has improved solutions of many computer vision problems including object detection. Object detection can exploit depth information and different encodings obtained from the depth map. Although previous works proved that depth information can be used to improve object detection results, this thesis investigates the effects of depth map to object detection from different aspects in detailed experiments. To clarify these effects, we examine the follow...
Moving hot object detection in airborne thermal videos Kaba, Utku; Akar, Gözde; Department of Electrical and Electronics Engineering (2012) In this thesis, we present an algorithm for vision based detection of moving objects observed by IR sensors on a moving platform. In addition we analyze the performance of different approaches in each step of the algorithm. The proposed algorithm is composed of preprocessing, feature detection, feature matching, homography estimation and difference image analysis steps. First, a global motion estimation based on planar homography model is performed in order to compensate the motion of the sensor and moving ...

Citation Formats

E. GÜNDOĞDU, A. KOÇ, and A. A. Alatan, “Automatic target recognition and detection in infrared imagery under cluttered background,” 2017, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/36040.