Abstract
In today’s era, the usage of videos has led to major contributions in the technological world. Be it CCTV footage captured in apartments and at traffic intersections or real-time recordings in the medical industry, videos help in discerning various patterns and uncovering hidden details, accounting for its extensive usage. Future frame prediction involves using the analysis of videos to predict what could happen next in the video. This is an emerging field of deep learning and computer vision whose advancement would provide large potential to enable faster decision making processes in areas like autonomous driving cars, anomaly detection, and aggression in people and falls in the elderly. Due to the proven effectiveness of deep learning in image processing, our goal is to develop a generative model capable of producing a future frame given a set of sequential frames as input. Generative adversarial networks (GANs) and autoencoders are the models used in implementing and achieving the target results. This paper presents the analysis and results of different proposed architectures that take multiple frames as input and aim to produce accurate future frames with the use of multiple efficiency measures. A structural similarity index (SSIM) score of 0.875 was obtained for the predicted frame.
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Itagi, S., Gowda, S., Udupa, T., Shylaja, S.S. (2022). Future Frame Prediction Using Deep Learning. In: Raje, R.R., Hussain, F., Kannan, R.J. (eds) Artificial Intelligence and Technologies. Lecture Notes in Electrical Engineering, vol 806. Springer, Singapore. https://doi.org/10.1007/978-981-16-6448-9_21
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DOI: https://doi.org/10.1007/978-981-16-6448-9_21
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