Vol. 2 No. 10 (2024), Articles
Vol. 2 No. 10 (2024)

RECURRENT NEURAL NETWORK (RNN) AND LONG SHORT-TERM MEMORY (LSTM) MODELS FOR MOTION DETECTION IN VIDEO IMAGES

Articles
Published 2024-10-13
Muminov Islom
Teacher of specialized boarding school No. 1 of Termez city
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Keywords

Motion detection, Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), video processing, temporal dependencies, deep learning, object tracking, computer vision.

How to Cite

RECURRENT NEURAL NETWORK (RNN) AND LONG SHORT-TERM MEMORY (LSTM) MODELS FOR MOTION DETECTION IN VIDEO IMAGES. (2024). "XXI ASRDA INNOVATSION TEXNOLOGIYALAR, FAN VA TAʼLIM TARAQQIYOTIDAGI DOLZARB MUAMMOLAR" Nomli Respublika Ilmiy-Amaliy Konferensiyasi, 2(10), 118-122. https://universalpublishings.com/index.php/itfttdm/article/view/7505
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Abstract

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.        

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References

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