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|Title||UAV Detection Model Using Histogram of Oriented Gradients and SVM|
|Title in Arabic||نموذج لاكتشاف الطائرات بدون طيار باستخدام المدرج التكراري لاتجاهات الانحدار ومتجهات الدعم الآلي|
Object detection is a well-known challenge in computer vision, and this challenge becomes more complicated especially when the object of interest occupies a small portion of the field of view, possibly moving within complex backgrounds, and in wide illumination varieties. Solving such a difficult problem requires a detection algorithm that can tolerate shape variations due to rotation, transportation and other geometric transformations. Also depending on color information is not feasible because of variable illuminations diversity, especially in outdoor situations, and because of colorful range of UAVs' parts. A model that uses Histogram of Oriented Gradients (HOG) is proposed to extract UAVs' features, then SVM is employed to distinguish the UAVs specific features and detect them later. To enhance the detection accuracy some other supporting techniques were utilized such as image-pyramids and non-local maximum suppression. As the problem is relatively new, no standard or de-facto standard image library or datasets for UAVs detection benchmarking exists, and the limited amount of training UAV images is one of the biggest challenges. So, part of our work was to select a dataset of UAVs' images for training and testing purposes, then update it by combining images from already exist related researches and other sources. The used training and testing datasets should be realistic and reflect the real world challenges mentioned previously such as geometric transformations, variable illumination, and others. Our Model achieved a good performance, about 0.57 F1 score counting false positives per image. The results are comparable to more modern techniques that are based on convolutional neural networks. The model can still be further improved, by utilizing other techniques such as ensemble of exemplar SVMs, or using discriminative part training techniques with latent SVM.
|Publisher||الجامعة الإسلامية بغزة|
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