Pedestrian re-recognition method and system based on multi-channel consistency features
A pedestrian re-identification and consistency technology, applied in the field of deep learning, can solve problems such as inability to re-identify pedestrians with high precision
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Embodiment 1
[0075] A pedestrian re-identification method based on multi-channel consistency features, comprising the following steps:
[0076] Step 1: Input N image pairs to be matched including training data and test data and its corresponding label l n , where n=1,...,N.
[0077] The second step: extracting the semantic feature representation and the color texture spatial distribution feature representation of the image data input in the first step, specifically including the following steps:
[0078] 1) Extract the semantic feature representation of the image data:
[0079]
[0080] in, is the semantic feature representation of the input image pair, f CNN Indicates the convolution operation, is the parameter to be learned;
[0081] 2) Extract the spatial distribution characteristics of image data in each channel of RGB, HSV (color information), SILTP (texture information), and perform feature extraction through a convolutional neural network composed of three convolutional ...
Embodiment 2
[0105] A pedestrian re-identification system based on multi-channel consistency features, including the following modules:
[0106] The image data input module is used to input N image pairs to be matched including training data and test data and its corresponding label ln , where n=1,...,N;
[0107] The feature representation extraction module is used to extract the semantic feature representation and color texture spatial distribution feature representation of the image data input by the image data input module;
[0108] A consistent feature representation module, configured to obtain a consistent feature representation of the semantic feature representation and color texture spatial distribution feature representation through multi-scale feature matching;
[0109] The probability representation output module is used to construct a binary classifier for the consistent feature representation obtained by the consistent feature representation module, and output a probability ...
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