Please use this identifier to cite or link to this item: https://dspace.ncfu.ru/handle/20.500.12258/26576
Title: Raw Data Point Cloud Probabilistic Filtering Algorithm
Authors: Kalita, D. I.
Калита, Д. И.
Lyakhov, P. A.
Ляхов, П. А.
Nagornov, N. N.
Нагорнов, Н. Н.
Keywords: Accuracy;Moving object;Camera calibration;Iterative division process;Kalman filter;Lidar sensor;Point cloud
Issue Date: 2023
Citation: Kalita, D.I., Lyakhov, P.A., Nagornov, N.N. Raw Data Point Cloud Probabilistic Filtering Algorithm // Proceedings of the Seminar on Signal Processing, SoSP 2023. - 2023. - pp. 27-31. - DOI: 10.1109/IEEECONF60473.2023.10366114
Series/Report no.: Proceedings of the Seminar on Signal Processing, SoSP 2023
Abstract: Solving the problem of detecting a moving object in a video stream in real time is one of the urgent tasks in computer vision systems. There are various ways, methods and computational algorithms for solving it. One of the promising algorithms for detecting and predicting the position of a moving object is the probabilistic Kalman filter. On the other hand, to detect a moving object and determine the distance to it, the approach of merging lidar and camera sensors is increasingly used. The Kalman filter can be applied as a suitable filtering algorithm capable of handling multiple inputs. This paper proposes a filtering algorithm based on the integration of probabilistic and median data filtering. The advantage of this approach is the replacement of the division operation in computational calculations by the Goldschmidt algorithm. The developed algorithm will reduce the delay time of the algorithm, as well as improve its accuracy. The results obtained can be effectively applied in various computer vision systems.
URI: http://hdl.handle.net/20.500.12258/26576
Appears in Collections:Статьи, проиндексированные в SCOPUS, WOS

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