The computational analysis, understanding, and manipulation of video data using machine learning and computer vision techniques. Core tasks include object detection and tracking, semantic segmentation, action recognition, temporal modelling, and scene understanding. Modern approaches employ 3D convolutional networks, vision transformers, and self-supervised learning on large-scale video datasets.
Semantic Classification
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Key Characteristics
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Analyzes spatial and temporal information jointly
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Employs 3D CNNs and temporal convolutional networks
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Handles variable-length sequences and frame rates
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Supports real-time processing for interactive applications
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Integrates multi-modal information (audio, text, visual)
Overview
Video Processing in AI involves computational analysis, understanding, and manipulation of video data using machine learning and computer vision techniques. Core tasks include object detection and tracking, action recognition, video segmentation, temporal modeling, scene understanding, and video generation. Modern approaches leverage 3D convolutional networks, recurrent architectures, transformers for temporal reasoning, and self-supervised learning on large video datasets. Applications span surveillance, autonomous driving, content moderation, sports analytics, medical imaging, and video editing automation.
Related Concepts
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References
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Carreira, J. & Zisserman, A. (2017). Quo Vadis, Action Recognition? A New Model and the Kinetics Dataset. CVPR 2017.
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Tran, D. et al. (2015). Learning Spatiotemporal Features with 3D Convolutional Networks. ICCV 2015.
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Arnab, A. et al. (2021). ViViT: A Video Vision Transformer. ICCV 2021.