Abstract
An algorithm for detecting and tracking moving people on video sequences using the block optical flow method and motion maps is proposed. To reduce time expenditures, a pyramidal representation of the frame and template search are used at the stage of building a preliminary map of motion vectors. The integral optical flow allows one to reduce the resulting amplitudes of the background displacement vectors and increase the resulting amplitudes of the displacement vectors of foreground objects. To improve the accuracy for localization of objects, the additive minimax similarity function is used in the analysis of motion vectors. Objects are tracked based on a modified tracing algorithm using the Kalman filter. The developed algorithm allows one not only to detect a moving object but also to show the trajectory of its movement. The results of experiments are presented that allow evaluating the effectiveness of the algorithm.
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Funding
This work is supported by Public Welfare Technology Applied Research Program of Zhejiang Province (LGF19F020016, LGJ18F020001 and LGJ19F020002) and the National High-end Foreign Experts Program (GDW20183300463).
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Conflict of interests. The authors declare that they have no conflicts of interest.Statement of compliance with standards of research involving humans as subjects. All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants involved in the study.
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Huafeng Chen. Born in 1982. Associate Professor of Zhejiang Shuren University. Graduated from Zhejiang University in 2003. Received his PhD in 2009 in the field of Earth Exploration and Information Technology at the Institute of Space Information & Technique, Zhejiang University. His scientific interests include remote sensing image processing, GIS application, image and video processing, and multiagent system. He has published more than ten academic articles.
Rykhard Bohush. Graduated from Polotsk State University in 1997. Received his PhD in 2002 in the field of Information Processing at the Institute of Engineering Cybernetics, the National Academy of Sciences of Belarus. Head of Computer Systems and Networks Department of Polotsk State University. His scientific interests include image and video processing, object representation and recognition, intelligent systems, and machine learning.
Chaoxiang Chen. Received her MS degree in software engineering from Hangzhou Dianzi University in Zhejiang, China. She is currently a professor and director of teaching quality monitoring center in Zhejiang Shuren University. Her research interest is mainly in the areas of graphics image and intelligent mining. She has completed several researches and published several academic papers in above research areas.
Sergey Ablameyko. Born in 1956, DipMath in 1978, PhD in 1984, DSc in 1990, Prof. in 1992. Professor of Belarusian State University. His scientific interests are image analysis, pattern recognition, digital geometry, knowledge-based systems, geographical information systems, and medical imaging. He is on the Editorial Board of Pattern Recognition Letters, Pattern Recognition and Image Analysis, and many other international and national journals. He is a senior member of IEEE, Fellow of IAPR, Fellow of the Belarusian Engineering Academy, Academician of the National Academy of Sciences of Belarus, Academician of the European Academy, and others. He was a First Vice-President of the International Association for Pattern Recognition IAPR (2006–2008), President of the Belarusian Association for Image Analysis and Recognition.
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Chen, H., Bohush, R.P., Chen, C. et al. Estimation of People Movement in Video Based on Optical Flow Block Method and Motion Maps. Pattern Recognit. Image Anal. 31, 261–270 (2021). https://doi.org/10.1134/S105466182102005X
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DOI: https://doi.org/10.1134/S105466182102005X