Jan 3, 2021 · The key challenges of lightweight compressed video action recognition are: 1) how to design a lightweight yet highly effective deep CNN model ...
Aug 13, 2020 · Abstract. Most existing action recognition models are large convolu- tional neural networks (CNNs) that work only with raw RGB frames as.
They explore the use of lightweight networks without affecting the classification performance to reduce the computational complexity of compressed video action ...
Compared to existing compressed video action recognition models, it is much more compact and faster thanks to adopting a lightweight CNN backbone.
Oct 17, 2021 · Lightweight action recognition in compressed videos. In European Conference on Computer Vision, pages 337–352. Springer, 2020. Barak Battash ...
Regarding action recognition in compressed data, I-frames (sparsely sampled RGB frames) and P-frames (e.g. motion vectors and residuals) are used for feature ...
Our goal is to design a computer vision system for action recognition that operates directly on the stored compressed video. The compression is solely designed ...
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Compared to existing compressed video action recognition models, it is much more compact and faster thanks to adopting a lightweight CNN backbone.
Jul 20, 2024 · Few-shot action recognition aims to address the high cost and impracticality of manually labeling complex and variable video data in action recognition.
They explore the use of lightweight networks without affecting the classification performance to reduce the computational complexity of compressed video action ...