Progressive vehicle searching method and device
First Claim
1. A vehicle searching method, characterized in that it comprises:
- obtaining a first image of a target vehicle;
extracting a first appearance visual feature of the target vehicle from the first image;
extracting a second appearance visual feature of the searched vehicle respectively from several second images;
wherein, the second images are the images stored in a vehicle monitoring image database;
calculating an appearance similarity distance between the first image and each of the second images according to the first appearance visual feature and each of the second appearance visual features;
selecting several images from the several second images as several third images;
determining a first license plate area in the first image and a second license plate area in each of the third images;
obtaining a first license plate feature corresponding to the first license plate area and a second license plate feature corresponding to each of the second license plate areas by inputting the first license plate area and each of the second license plate areas respectively into a preset Siamese neural network model;
calculating a license plate feature similarity distance between the first image and each of the third images according to the first license plate feature and each of the second license plate features;
calculating a visual similarity distance between the first image and each of the third images according to the appearance similarity distance and the license plate feature similarity distance;
obtaining a first search result of the target vehicle by arranging the several third images in an ascending order of the visual similarity distances;
wherein, the first appearance visual feature comprises a first texture feature, a first color feature and a first semantic attribute feature;
the second appearance visual feature comprises a second texture feature, a second color feature and a second semantic attribute feature;
the step of calculating an appearance similarity distance between the first image and each of the second images according to the first appearance visual feature and each of the second appearance visual features, comprises;
performing the following steps for the first image and each of the second images respectively;
calculating a texture similarity distance according to the first texture feature and the second texture feature;
calculating a color similarity distance according to the first color feature and the second color feature;
calculating a semantic attribute similarity distance according to the first semantic attribute feature and the second semantic attribute feature;
calculating the appearance similarity distance between the first image and the second image according to the texture similarity distance, the color similarity distance, the semantic attribute similarity distance and a third preset model;
wherein, the third preset model is;
Dappearance =α
×
dtexture +β
×
dcolor +(1−
α
−
β
)×
dattribute, wherein, Dappearance is the appearance similarity distance, d texture, is the texture similarity distance, dcolor is the color similarity distance, dattribute is the semantic attribute similarity distance, and are empirical weights.
1 Assignment
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Accused Products
Abstract
The present application discloses a vehicle searching method and device, which can perform the steps of: calculating an appearance similarity distance between a first image of a target vehicle and several second images containing the searched vehicle; selecting several images from the several second images as several third images; obtaining corresponding license plate features of license plate areas in the first image and each of the third images with a preset Siamese neural network model; calculating a license plate feature similarity distance between the first image and each of the third images according to license plate feature; calculating a visual similarity distance between the first image and each of the third images according to the appearance similarity distance and the license plate feature similarity distance; obtaining a the first search result of the target vehicle by arranging the several third images in an ascending order of the visual similarity distances. The solution provided by the present application is not limited by application scenes, and it also improves vehicle searching speed and accuracy while reducing requirements of hardware such as cameras that collect images of a vehicle and auxiliary devices.
13 Citations
10 Claims
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1. A vehicle searching method, characterized in that it comprises:
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obtaining a first image of a target vehicle; extracting a first appearance visual feature of the target vehicle from the first image; extracting a second appearance visual feature of the searched vehicle respectively from several second images;
wherein, the second images are the images stored in a vehicle monitoring image database;calculating an appearance similarity distance between the first image and each of the second images according to the first appearance visual feature and each of the second appearance visual features; selecting several images from the several second images as several third images; determining a first license plate area in the first image and a second license plate area in each of the third images; obtaining a first license plate feature corresponding to the first license plate area and a second license plate feature corresponding to each of the second license plate areas by inputting the first license plate area and each of the second license plate areas respectively into a preset Siamese neural network model; calculating a license plate feature similarity distance between the first image and each of the third images according to the first license plate feature and each of the second license plate features; calculating a visual similarity distance between the first image and each of the third images according to the appearance similarity distance and the license plate feature similarity distance; obtaining a first search result of the target vehicle by arranging the several third images in an ascending order of the visual similarity distances; wherein, the first appearance visual feature comprises a first texture feature, a first color feature and a first semantic attribute feature; the second appearance visual feature comprises a second texture feature, a second color feature and a second semantic attribute feature; the step of calculating an appearance similarity distance between the first image and each of the second images according to the first appearance visual feature and each of the second appearance visual features, comprises; performing the following steps for the first image and each of the second images respectively; calculating a texture similarity distance according to the first texture feature and the second texture feature; calculating a color similarity distance according to the first color feature and the second color feature; calculating a semantic attribute similarity distance according to the first semantic attribute feature and the second semantic attribute feature; calculating the appearance similarity distance between the first image and the second image according to the texture similarity distance, the color similarity distance, the semantic attribute similarity distance and a third preset model;
wherein, the third preset model is;Dappearance =α
×
dtexture +β
×
dcolor +(1−
α
−
β
)×
dattribute, wherein, Dappearance is the appearance similarity distance, d texture, is the texture similarity distance, dcolor is the color similarity distance, dattribute is the semantic attribute similarity distance, and are empirical weights. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A vehicle searching device, characterized in that it comprises a processor, coupled to a memory, that executes or facilitates execution of executable modules, the executable modules comprising:
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a first image obtaining module for obtaining a first image of a target vehicle; a first appearance feature extracting module for extracting a first appearance visual feature of the target vehicle from the first image; a second appearance feature extracting module for extracting a second appearance visual feature of the searched vehicle respectively from several second images;
wherein, the second images are the image stored in a vehicle monitoring image database;a first calculating module, for calculating an appearance similarity distance between the first image and each of the second images according to the first appearance visual feature and each of the second appearance visual features; a first selecting module for selecting several images from the several second images as several third images; a license plate area determining module, for determining a first license plate area in the first image and a second license plate area in each of the third images; a license plate feature obtaining module, for obtaining a first license plate feature corresponding to the first license plate area and a second license plate feature corresponding to each of the second license plate areas by inputting the first license plate area and each of the second license plate areas respectively into a preset Siamese neural network model; a second calculating module, for calculating a license plate feature similarity distance between the first image and each of the third images according to the first license plate feature and each of the second license plate features; a third calculating module, for calculating a visual similarity distance between the first image and each of the third images according to the appearance similarity distance and the license plate feature similarity distance; a first search result obtaining module, for obtaining a first search result of the target vehicle by arranging the several third images in an ascending order of the visual similarity distances; wherein, the first appearance visual feature comprises a first texture feature, a first color feature and a first semantic attribute feature; the second appearance visual feature comprises a second texture feature, a second color feature and a second semantic attribute feature; the step of calculating an appearance similarity distance between the first image and each of the second images according to the first appearance visual feature and each of the second appearance visual features, comprises; performing the following steps for the first image and each of the second images respectively; calculating a texture similarity distance according to the first texture feature and the second texture feature; calculating a color similarity distance according to the first color feature and the second color feature; calculating a semantic attribute similarity distance according to the first semantic attribute feature and the second semantic attribute feature; calculating the appearance similarity distance between the first image and the second image according to the texture similarity distance, the color similarity distance, the semantic attribute similarity distance and a third preset model;
wherein, the third preset model is;Dappearance =α
×
dtexture +β
×
dcolor +(1−
α
−
β
)×
dattribute , wherein, Dappearance is the appearance similarity distance, dtexture is the texture similarity distance, dcolor is the color similarity distance, dattribute is the semantic attribute similarity distance, and are empirical weights. - View Dependent Claims (10)
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Specification