Article 5417

Title of the article



Shepelev Kirill Valer'evich, Master’s degree student, Penza State University (40 Krasnaya street, Penza, Russia),

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Background. The current accuracy of moving objects detection via open source software does not exceed 60 %. This problem has several aspects. The first aspect is a lack of algorithms and software for import substitution, the second is a high cost of creating a separate video surveillance system with moving object detecting and classifying functions.
Materials and methods. For the purpose of detecting and classifying moving objects in a video sequence the author uses a motion detection algorithm by calculating static pixel values over a mixture of normal distributions and an algorithm for classifying images. The software implementation of the system is performed by means of OPENCV computer vision library.
Results and conclusions. The work suggests a model of video sequence representation in motion detection problems based on a three-dimensional dynamic information array of pixel brightness levels. It is distinguished by an updated zero motion mask frame and allows increasing the accuracy of object detection by dynamically refining the static part of a video sequence using a low-pass filter. The author has developed and implemented a motion detection algorithm based on estimating the probability density of static pixels falling out in a video sequence. The algorithm is distinguished by the use of a mix of normal distributions of brightness levels of
information field elements. A combined method for detecting and classifying moving objects in a video sequence is offered. The accuracy of detection of objects based on the combined method increases by 15 % compared to the accuracy of a simple motion detector and by 5 % relative to the accuracy of the Haar cascade classification

Key words

detection and classification of moving objects in a video sequences, video analysis, video surveillance, computer vision

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Дата создания: 27.03.2018 10:15
Дата обновления: 27.03.2018 10:56