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National Repository of Grey Literature 3 records found  Search took 0.00 seconds. 
Passive optical detection and classification of flying objects
 Mošková, Andrea ; Marcoň, Petr (referee) ; Vlachová Hutová, Eliška (advisor)
Based on an initial analysis of the methods used for optical detection and recognition of moving elements in a dynamic image captured by a static camera, the bachelor's thesis presents a proposal for a complex detection algorithm capable of recording and classifying flying objects in the sky. The method uses a differential detection algorithm to detect signs of motion, thanks to which flying objects are located in the video frames. The SVM classifier then decides on the classification of the object into one of the three considered classes, based on its description obtained by extraction of SIFT descriptors and possibly supplemented with information from RGB histograms. The algorithm was implemented primarily in Matlab.
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Passive optical detection and classification of flying objects
 Mošková, Andrea ; Vlachová Hutová, Eliška
The article presents our solution for the classification of moving flying objects in a video sequence captured by a static camera. The tool uses the extraction of scale and rotation invariant SIFT features, which allow the multi-class SVM to classify the examined object into one of the considered classes: ‘bird’, ‘plane’ or ‘negative’. The most successful of our tested models achieved accuracy of over 90% and their recall and precision for each class reached values above 90%.
Detailed record
Passive optical detection and classification of flying objects
 Mošková, Andrea ; Marcoň, Petr (referee) ; Vlachová Hutová, Eliška (advisor)
Based on an initial analysis of the methods used for optical detection and recognition of moving elements in a dynamic image captured by a static camera, the bachelor's thesis presents a proposal for a complex detection algorithm capable of recording and classifying flying objects in the sky. The method uses a differential detection algorithm to detect signs of motion, thanks to which flying objects are located in the video frames. The SVM classifier then decides on the classification of the object into one of the three considered classes, based on its description obtained by extraction of SIFT descriptors and possibly supplemented with information from RGB histograms. The algorithm was implemented primarily in Matlab.
Detailed record

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