title: Ð ÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ñ Ð°Ð»Ð³Ð¾ÑÐ¸ÑÐ¼Ð° ÑÐ¾Ð¿ÑÐ¾Ð²Ð¾Ð¶Ð´ÐµÐ½Ð¸Ñ Ð½ÐµÑÐºÐ¾Ð»ÑÐºÐ¸Ñ Ð¾Ð±ÑÐµÐºÑÐ¾Ð² Ð½Ð° Ð¿Ð¾ÑÐ»ÐµÐ´Ð¾Ð²Ð°ÑÐµÐ»ÑÐ½Ð¾ÑÑÐ¸ Ð°ÑÑÐ¾ÑÐ¾ÑÐ¾ÑÐ½Ð¸Ð¼ÐºÐ¾Ð²
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Implementation of an algorithm for tracking several objects on a sequence of aerial photographs
Creators: name: ÐÐµÐ·Ð±Ð¾ÑÐ¾Ð´Ð¾Ð², ÐÐ½Ð´ÑÐµÐ¹
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2020
Subjects: Ð¾Ð±Ð½Ð°ÑÑÐ¶ÐµÐ½Ð¸Ðµ
ÑÐ°ÑÐ¿Ð¾Ð·Ð½Ð°Ð²Ð°Ð½Ð¸Ðµ
ÑÐ¾Ð¿ÑÐ¾Ð²Ð¾Ð¶Ð´ÐµÐ½Ð¸Ðµ
ÑÐ²ÑÑÑÐ¾ÑÐ½Ð°Ñ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð°Ñ ÑÐµÑÑ
Ð°ÑÑÐ¾ÑÐ¾ÑÐ¾ÑÐ½Ð¸Ð¼Ð¾Ðº
YOLO v2
detection
recognition
multiple object tracking
convolutional neural network
sequence of aerial photographs

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description: The given work is devoted to the implementation of a neural network algorithm for detecting and tracking a group of moving objects of complex shape in a sequence of aerial photographs. The subject of the study is moving objects observed because of aerial photography using a camera mounted on board of an aircraft. At the first stage, after a review of existing approaches, the most optimal way to detect objects on the frames of the input video sequence was chosen. According to the results of the analysis, it was found that in a complex back-ground-target environment in on-board television systems, it is most effective to use neural network approaches, such as the YOLO family. The Kuhn â Munkres algorithm was used to compare unique moving ob-jects from frame to frame. It is proposed to perform the processing of problem situations related to occlusions and repeated appearances of objects by com-paring histograms of detected objects. The final stage of the work was an assessment of the accuracy of the algorithm using the appropriate metrics on 10 test video sequences that characterize various problem scenarios resulting from object tracking results. Based on the results, the proposed approach showed high results in comparison with the TLD and IOU Tracker algorithms. It was concluded that the proposed approach is effective in the conditions of aerial photography and a moving camera. The developed algorithm can be used in the tasks of tracking certain moving objects during their movement when shooting from the board of an aircraft, as well as for real-time monitoring the perimeter of protected objects.