Abstract: This study proposes a scalable and hardware-adaptable approach to automatic video caption generation by comparing two architectures: a traditional encoder–decoder framework combining InceptionResNetV2 with GRU and a transformer-based model integrating TimeSformer with GPT-2. The system supports CPU and GPU deployment through a unified pipeline built on FFmpeg and ImageMagick for keyframe extraction and subtitle embedding. Experimental evaluations on the MSVD and VATEX datasets demonstrate that the TimeSformer–GPT-2 architecture significantly outperforms baseline models, particularly in GPU settings, achieving top results across BLEU, METEOR, ROUGE-L, and CIDEr metrics. This superiority is attributed to its capacity to model spatiotem-poral dependencies and generate contextually rich language. Designed for real-time operation, the system is also suitable for low-resource devices, enabling impactful applications such as assistive tools for the visually impaired and intelligent video indexing. Despite high computational demands and sequence-length limitations, the system presents promising directions for future development, including multilingual captioning, multimodal audio–visual integration, and lightweight models like TinyGPT for enhanced portability.

Othmane Sebban, Ahmed Azough and Mohamed Lamrini, “Real-Time Video Captioning on CPU and GPU: A Comparative Study of Classical and Transformer Models” International Journal of Advanced Computer Science and Applications(IJACSA), 16(6), 2025. http://dx.doi.org/10.14569/IJACSA.2025.0160681

@article{Sebban2025,
title = {Real-Time Video Captioning on CPU and GPU: A Comparative Study of Classical and Transformer Models},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2025.0160681},
url = {http://dx.doi.org/10.14569/IJACSA.2025.0160681},
year = {2025},
publisher = {The Science and Information Organization},
volume = {16},
number = {6},
author = {Othmane Sebban and Ahmed Azough and Mohamed Lamrini}
}