Multidimensional characteristic passenger re-identification method, device, storage medium and processor
A pedestrian re-identification and multi-scale feature technology, applied in the field of multi-scale feature pedestrian re-identification, can solve the problem of low recognition accuracy
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Embodiment 1
[0027] According to an embodiment of the present invention, an embodiment of a method for multi-scale feature pedestrian re-identification is provided. It should be noted that the steps shown in the flow chart of the accompanying drawings can be implemented in a computer system such as a set of computer-executable instructions and, although a logical order is shown in the flowcharts, in some cases the steps shown or described may be performed in an order different from that shown or described herein.
[0028] figure 1 is a schematic flowchart of an optional multi-scale feature pedestrian re-identification method according to an embodiment of the present invention, as shown in figure 1 As shown, the method includes the following steps:
[0029] Step S102, establishing a first convolutional neural network model according to a preset multi-scale feature extraction method, wherein the input branch of the first arbitrary layer in the first convolutional neural network model includ...
Embodiment 2
[0058] According to another aspect of the embodiments of the present invention, a device for re-identifying pedestrians with multi-scale features is also provided, such as Figure 5 As shown, the device includes:
[0059] The first construction unit 501 is configured to establish a first convolutional neural network model according to a preset multi-scale feature extraction method, wherein the input branch of the first arbitrary layer in the first convolutional neural network model includes at least one front bit of the first arbitrary layer The output branch of the level; the first training unit 503 is used to train the first convolutional neural network model according to the preset public data set to obtain the second convolutional neural network model, wherein the second convolutional neural network model reaches a state of convergence The first convolutional neural network model; the second training unit 505 is used to train the model group consisting of the second convolut...
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