Generative adversarial network for video analytics

Sasithradevi, A.; Roomi, S. Mohamed Mansoor; Sivaranjani, R.

doi:10.1016/b978-0-12-823519-5.00008-7

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…In recent years, many works have focused on video generation using images of generalist topics, such as sports, urban scenes, people, cloud movement, or even fireworks [21,[25][26][27][28]. The aim of these works is to capture the movement of the objects appearing in the video, not their development over time.…”

Section: Video Generationmentioning

confidence: 99%

Temporal Development GAN (TD-GAN): Crafting More Accurate Image Sequences of Biological Development

Celard,

Seara Vieira,

Sorribes-Fdez

et al. 2023

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In this study, we propose a novel Temporal Development Generative Adversarial Network (TD-GAN) for the generation and analysis of videos, with a particular focus on biological and medical applications. Inspired by Progressive Growing GAN (PG-GAN) and Temporal GAN (T-GAN), our approach employs multiple discriminators to analyze generated videos at different resolutions and approaches. A new Temporal Discriminator (TD) that evaluates the developmental coherence of video content is introduced, ensuring that the generated image sequences follow a realistic order of stages. The proposed TD-GAN is evaluated on three datasets: Mold, Yeast, and Embryo, each with unique characteristics. Multiple evaluation metrics are used to comprehensively assess the generated videos, including the Fréchet Inception Distance (FID), Frechet Video Distance (FVD), class accuracy, order accuracy, and Mean Squared Error (MSE). Results indicate that TD-GAN significantly improves FVD scores, demonstrating its effectiveness in generating more coherent videos. It achieves competitive FID scores, particularly when selecting the appropriate number of classes for each dataset and resolution. Additionally, TD-GAN enhances class accuracy, order accuracy, and reduces MSE compared to the default model, demonstrating its ability to generate more realistic and coherent video sequences. Furthermore, our analysis of stage distribution in the generated videos shows that TD-GAN produces videos that closely match the real datasets, offering promising potential for generating and analyzing videos in different domains, including biology and medicine.

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