Abstract
Fast and computationally less complex feature extraction for moving object detection using aerial images from unmanned aerial vehicles (UAVs) remains as an elusive goal in the field of computer vision research. The types of features used in current studies concerningmoving object detection are typically chosen based on improving detection rate rather than on providing fast and computationally less complex feature extraction methods. Because moving object detection using aerial images from UAVs involves motion as seen from a certain altitude, effective and fast feature extraction is a vital issue for optimum detection performance. This research proposes a two-layer bucket approach based on a new feature extraction algorithm referred to as the moment-based feature extraction algorithm (MFEA). Because a moment represents thecoherent intensity of pixels and motion estimation is a motion pixel intensity measurement, this research used this relation to develop the proposed algorithm. The experimental results reveal the successful performance of the proposed MFEA algorithm and the proposed methodology.
Highlights
The significance of feature extraction using aerial images from unmanned aerial vehicles (UAVs) has increased in the field of computer vision with the development of moving object detection algorithms using aerial images
This paper presents a two-layer bucket (TLB) approach based on a new feature extraction algorithm named the moment-based feature extraction algorithm (MFEA), which is expected to PLOS ONE | DOI:10.1371/journal.pone
Because this work used aerial images, we developed a raw-coded frame extractor and denoise tools using a median filter for the experimental analysis
Summary
The significance of feature extraction using aerial images from unmanned aerial vehicles (UAVs) has increased in the field of computer vision with the development of moving object detection algorithms using aerial images. The purpose of efficient feature extraction is to facilitate fast moving object extraction using aerial images from UAVs in the frame achieved via two-frame difference methods. Appropriate feature selection is a challenging task due to the large number of features that can be extracted, which requires a substantial amount of processing time during the detection process. The significance of feature extraction using aerial images has increased with the development of aerial image-based moving object detection in the computer vision research field. The purpose of efficient feature extraction is to facilitate fast moving object extraction from aerial images in a given frame based on frame difference methods. Appropriate feature selection is a challenging task due to the large number of features present in a typical frame,requiringa significant amount of processing time during the detection process. Because motion detection and the detection of a moving object are coupled, a less complex feature extraction algorithm is needed to ensure proper motion estimation and the detection of objects with less computation time and lower computational complexity
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