RESEARCH ON THE OPTICAL FLOW METHOD FOR DETECTING CHANGES IN THE ANGLE OF VIEW FROM VIDEO CAMERAS IN URBAN ENVIRONMENTS
Authors:
1.
Akademiya nauk Respubliki Tatarstan
GBU «Bezopasnost' dorozhnogo dvizheniya»
GBU «Bezopasnost' dorozhnogo dvizheniya»
2.
Kazan National Research Technical University named after A.N. Tupolev
3.
Kazan National Research Technical University named after A.N. Tupolev
4.
Kazan State Energy University
5.
KNITU-KAI
6.
GBU «Bezopasnost' dorozhnogo dvizheniya»
Type:
Article
Pages:
from 81
to 85
Status:
Published
Received:
24.12.2025
Accepted:
25.12.2025
Published:
25.12.2025
Language:
Russian
Keywords:
VIDEO SURVEILLANCE, SHOOTING ANGLE, MACHINE VISION, VIDEO IMAGE PROCESSING, OPTICAL FLOW METHOD, DETECTION OF CHANGES IN THE CAMERA ANGLE
Abstract (English):
The article is devoted to the study of the optical flow method for detecting changes in the shooting angle of city surveillance cameras. The study was conducted using data obtained from surveillance cameras in Kazan. The study was conducted under conditions of a static position of the cameras, as well as under conditions of their vertical and horizontal shift by 10 to 50 degrees. For each of the conditions, the corresponding data samples were used. To study the optical flow method, four algorithms for its implementation were used. Two algorithms were used for a sparse flow (Lucas-Kanade and Pyramidal Lucas-Kanade), and two more were used for a dense flow (Dense Inverse Search and FarneBack). To evaluate their effectiveness, and, consequently, the optical flow method, the following metrics were calculated: flow value, execution time in seconds, average shift value, proportion of non-zero vectors, shift consistency. Based on the obtained results, several important conclusions were made. With a static position of the camera, the average shift value for dense flows was significantly higher than for sparse flows. This increases the chance of obtaining false positive results, especially at night. With controlled camera displacement, the average shift value for the Lucas-Kanade and FarneBack algorithms was almost zero, which indicates their inability to reliably detect displacement. The Dense Inverse Search algorithm turned out to be the most sensitive to displacements, and Pyramidal Lucas-Kanade detected displacements, but with less accuracy. Dense flow algorithms are inferior in speed to sparse flow algorithms. Optical flow algorithms do not respond to text elements of the image. Thus, the study solved the problem. The study allowed us to evaluate the effectiveness of the selected method and algorithms in terms of the accuracy of calculating the average shift and resistance to dynamic objects. The optical flow method and its algorithmic implementation Pyramidal Lucas-Kanade are best suited for detecting the displacement of CCTV cameras in an urban environment. Minimization of false positives when the camera is stationary, combined with reliable detection of shifts, makes it an optimal choice, and immunity to text elements in the image increases its effectiveness.
The article is devoted to the study of the optical flow method for detecting changes in the shooting angle of city surveillance cameras. The study was conducted using data obtained from surveillance cameras in Kazan. The study was conducted under conditions of a static position of the cameras, as well as under conditions of their vertical and horizontal shift by 10 to 50 degrees. For each of the conditions, the corresponding data samples were used. To study the optical flow method, four algorithms for its implementation were used. Two algorithms were used for a sparse flow (Lucas-Kanade and Pyramidal Lucas-Kanade), and two more were used for a dense flow (Dense Inverse Search and FarneBack). To evaluate their effectiveness, and, consequently, the optical flow method, the following metrics were calculated: flow value, execution time in seconds, average shift value, proportion of non-zero vectors, shift consistency. Based on the obtained results, several important conclusions were made. With a static position of the camera, the average shift value for dense flows was significantly higher than for sparse flows. This increases the chance of obtaining false positive results, especially at night. With controlled camera displacement, the average shift value for the Lucas-Kanade and FarneBack algorithms was almost zero, which indicates their inability to reliably detect displacement. The Dense Inverse Search algorithm turned out to be the most sensitive to displacements, and Pyramidal Lucas-Kanade detected displacements, but with less accuracy. Dense flow algorithms are inferior in speed to sparse flow algorithms. Optical flow algorithms do not respond to text elements of the image. Thus, the study solved the problem. The study allowed us to evaluate the effectiveness of the selected method and algorithms in terms of the accuracy of calculating the average shift and resistance to dynamic objects. The optical flow method and its algorithmic implementation Pyramidal Lucas-Kanade are best suited for detecting the displacement of CCTV cameras in an urban environment. Minimization of false positives when the camera is stationary, combined with reliable detection of shifts, makes it an optimal choice, and immunity to text elements in the image increases its effectiveness.
Keywords:
VIDEO SURVEILLANCE, SHOOTING ANGLE, MACHINE VISION, VIDEO IMAGE PROCESSING, OPTICAL FLOW METHOD, DETECTION OF CHANGES IN THE CAMERA ANGLE
VIDEO SURVEILLANCE, SHOOTING ANGLE, MACHINE VISION, VIDEO IMAGE PROCESSING, OPTICAL FLOW METHOD, DETECTION OF CHANGES IN THE CAMERA ANGLE
