(untitled) ("XXI ASRDA INNOVATSION TEXNOLOGIYALAR, FAN VA TAʼLIM TARAQQIYOTIDAGI DOLZARB MUAMMOLAR" nomli respublika ilmiy-amaliy konferensiyasi, 2024)
This article explores the application of Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) models for motion detection in video images. These deep learning models are particularly suited for tasks that require understanding temporal dependencies, such as tracking objects and detecting movements over time in video streams. The paper discusses the underlying architecture of RNNs and LSTMs, their strengths in handling sequential data, and their application in motion detection tasks, along with performance evaluations.