A METHOD FOR SEARCHING AND RECOGNISING OBJECTS IN A VIDEO STREAM BY CALCULATING INTERFRAME DELTAS
DOI:
https://doi.org/10.26906/SUNZ.2025.2.249Keywords:
machine learning, computer vision, image processing, convolutional neural networks, visual image recognition, telecommunication systemsAbstract
The article proposes an improved method for searching and recognising objects in a video stream in real time using the calculation of interframe changes (deltas) and a neural classifier. The main goal of the study is to achieve high performance and reduce the computational load on system resources while maintaining acceptable accuracy. An experimental comparison with the basic SSD (Single Shot MultiBox Detector) method was carried out, which measured the following indicators: average frame processing time, RAM and video memory usage, CPU and graphics load, and recognition accuracy. Unlike SSDs, the proposed approach provides a higher processing speed (up to 35% increase) with a slight decrease in accuracy (less than 4%), which is compensated for by further adaptation of the model. At the same time, the use of the CPU and RAM increases by only 0.5-5%, while the amount of video memory consumed decreases by 57%. The study confirms the feasibility of using the improved delta classification method in video analytics systems with limited resources. This method can be integrated into applied security, video surveillance, and real-time intelligent monitoring systems.Downloads
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