Network live video feature extraction method in complex scene based on joint attention ResNeSt
A network live broadcast and video feature technology, applied in image communication, selective content distribution, electrical components, etc., can solve the problems that it is difficult to effectively learn spatio-temporal context information and affect the accuracy rate, so as to save computing resources, enhance effective extraction, The effect of good discrimination
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[0030]According to the above description, the following is a specific implementation process, but the range protected by this patent is not limited to the implementation process. Below is a specific workflow of the present invention:
[0031]The video data used in the present invention is derived from a number of network video platforms, and performs keyframe extraction of various live video downloads. During the experiment, take a key frame with 5FPS, and take only a segment of the continuous 16 frames to represent the video, and the video frame data of 224 × 224 pixels is obtained by Resize. Place the video frame data into the feature pyramid in the pyramid, to obtain a feature map of different scales; then through the calculation of the joint attention mechanism, obtain the attention weight allocation of multi-scale features; final combination convolution and poolization operation Setting a resnest module, through the overlapping of 50 RESNEST modules, a resnest50 feature extraction...
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