Method for Preprocessing Video Data for Training Deep-Learning Models for Identifying Behavioral Events in Bio-Objects

Marina Barulina (), Alexander Andreev, Ilya Kovalenko, Ilya Barmin, Eduard Titov and Danil Kirillov
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Marina Barulina: Institute of Physics and Mathematics, Perm State University, 15 Ul. Bukireva, 614068 Perm, Russia
Alexander Andreev: Faculty of Chemistry, Perm State University, 15 Ul. Bukireva, 614068 Perm, Russia
Ilya Kovalenko: Institute of Physics and Mathematics, Perm State University, 15 Ul. Bukireva, 614068 Perm, Russia
Ilya Barmin: Institute of Physics and Mathematics, Perm State University, 15 Ul. Bukireva, 614068 Perm, Russia
Eduard Titov: Institute of Physics and Mathematics, Perm State University, 15 Ul. Bukireva, 614068 Perm, Russia
Danil Kirillov: Institute of Physics and Mathematics, Perm State University, 15 Ul. Bukireva, 614068 Perm, Russia

Mathematics, 2024, vol. 12, issue 24, 1-18

Abstract: Monitoring moving bio-objects is currently of great interest for both fundamental and practical research. The advent of deep-learning algorithms has made it possible to automate the qualitative and quantitative analysis of the behavior of bio-objects recorded in video format. When processing such data, it is necessary to consider additional factors, such as background noise in the frame, the speed of the bio-object, and the need to reflect information about the previous (past) and subsequent (future) pose of the bio-object in one video frame. The preprocessed dataset must be suitable for verification by experts. This article proposes a method for preprocessing data to identify the behavior of a bio-object, a clear example of which is experiments on laboratory animals with the collection of video data. The method is based on combining information about a behavioral event presented in a sequence of frames with the addition of a native image and subsequent boundary detection using the Sobel filter. The resulting representation of a behavioral event is easily perceived by both human experts and neural networks of various architectures. The article presents the results of training several neural networks on the obtained dataset and proposes an effective neural network architecture (F1-score = 0.95) for identifying discrete events of biological objects’ behavior.

Keywords: deep-learning models; video data preprocessing; pose reflection; moving bio-objects; data-preprocessing method (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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