Real-time determination of object size in video images Bobomurodov Kamoliddin Abstract

Metodology: The methodology for real-time determination of object size in video images typically involves the following steps:

Load the video stream or image sequence.

Extract features from the video frames, such as edges, corners, or color information.
Use computer vision techniques to segment the object of interest from the background, such as thresholding or region growing.
Estimate the size of the object based on its relative distance from the camera and the size of the extracted features.
Display the video stream or image sequence with the detected object size or output the results to a file or database.
In more advanced systems, machine learning algorithms can be used to improve the accuracy of object detection and size estimation. These algorithms require a large dataset of labeled images and can be trained to recognize specific objects and estimate their size based on their visual appearance.
References

1. 
   S. Kiranyaz, M. Gabbouj, and M. Ince, "Real-time object size measurement using deep neural networks," Proceedings of the IEEE International Conference on Industrial Technology, pp. 537-542, 2016.
   

2. 
   J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You only look once: Unified, real-time object detection," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779-788, 2016.
   

3. 
   T. Park, J. Lim, C. Kim, and H. Kim, "Real-time object detection and tracking for autonomous driving systems using deep learning," Applied Sciences, vol. 10, no. 16, pp. 5417, 2020.
   

4. 
   OpenCV documentation: https://opencv.org/documentation/
   

5. 
   Python programming language: https://www.python.org/