Two-stage anchor-based dynamic video abstraction method

Abstract

The invention relates to a two-stage anchor-based dynamic video abstraction method. The method comprises the following steps: (1) constructing an anchor-based neural network, wherein the neural network comprises an extraction network, a self-attention mechanism network, a two-stage network, namely a region recommendation network and a regression classification network; and (2) determining types and lengths of anchors in the region recommendation network and the regression classification network, completing classification of positive and negative samples, training the anchor-based neural network constructed in the step (1), fixing trained parameters, obtaining two-stage anchor-based neural network models, and performing dynamic video abstraction by using the obtained neural network models. The brand new network structure provided by the invention can process all video frames in parallel, so that the training time is reduced, the final output accuracy is effectively improved, and the network structure is relatively simple and has low requirements on computing power.

Description

technical field [0001] The present invention relates to a video summarization method, in particular to a two-stage anchor-based dynamic video summarization method. Background technique [0002] With the development of science and technology, camera equipment has become popular, and the network for uploading and downloading videos has become smoother, followed by an increasing number of video resources. Therefore, this technical requirement has emerged in real life - greatly shortening the length of the video while expressing the main meaning of the original video. The emergence of video summarization methods has successfully responded to this demand. At present, video summarization technology has been successfully applied in many fields, such as surveillance video analysis, generation of movie trailers, motion recognition and so on. [0003] At present, there are mainly two types of methods for researching video summarization: 1) Static video summarization methods, also kno...

AI Technical Summary

Problems solved by technology

But the network cannot run in parallel, so its performance is largely limited

Later, a bidirectional long-short-term memory network was proposed, Bi-directional Long Short-Term Memory, referred to as BiLSTM, which is composed of forward LSTM and backward LSTM, which successfully solved the disadvantage that LSTM cannot run in parallel, but then The problem is that the network implementation becomes quite complex and has high computational requirements

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