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TransNet V2: An effective deep network architecture for fast shot transition detection

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arxiv 2008.04838 v1 pith:RNCY3YV7 submitted 2020-08-11 cs.CV

Tomáš Souček , Jakub Lokoč This is my paper
classification cs.CV
keywords shotdeepdetectionmodelnetworktransitionaccuracyapproaches
verification ladder T0 review T1 audit T2 compute T3 formal
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Although automatic shot transition detection approaches are already investigated for more than two decades, an effective universal human-level model was not proposed yet. Even for common shot transitions like hard cuts or simple gradual changes, the potential diversity of analyzed video contents may still lead to both false hits and false dismissals. Recently, deep learning-based approaches significantly improved the accuracy of shot transition detection using 3D convolutional architectures and artificially created training data. Nevertheless, one hundred percent accuracy is still an unreachable ideal. In this paper, we share the current version of our deep network TransNet V2 that reaches state-of-the-art performance on respected benchmarks. A trained instance of the model is provided so it can be instantly utilized by the community for a highly efficient analysis of large video archives. Furthermore, the network architecture, as well as our experience with the training process, are detailed, including simple code snippets for convenient usage of the proposed model and visualization of results.

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Cited by 6 Pith papers

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