Population density estimation method based on pyramid feature fusion
A technology of pyramid features and crowd density, applied in neural learning methods, computing, computer components, etc., can solve problems such as multi-scale target perception difficulties, and achieve the effect of enhancing perception, enhancing aggregation ability, and inhibiting activation.
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[0036] In this embodiment, convolutional features are extracted through the backbone, and then four parallel branches are used to further extract multi-scale features. At the same time, attention branches are introduced to focus on the area where the crowd is located. Finally, the multi-scale features are processed by the density map regressor to obtain the final result. The density map includes the following steps:
[0037] (1) Dataset generation: Process the annotation information of the public data set of the crowd, and use the Gaussian kernel to blur the annotation points in the annotation information to form the true density map required for training. Among them, for crowd images with high crowd density, use The geometric adaptive Gaussian kernel uses a Gaussian kernel with a standard deviation of 15 for crowd images with low crowd density; then the dataset is augmented by cropping and flipping to obtain more training data images;
[0038] (2) Feature extraction of backbo...
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