CN107992820B - Self-help goods selling method for container based on binocular vision - Google Patents
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Abstract
The invention discloses a self-service goods selling method for a container based on binocular vision, which comprises the following steps: shooting a first plane image and a second plane image of a target area of a vending machine or a sales counter through a binocular camera with fixed relative positions in the counter; processing the first plane image and the second plane image by using a binocular vision processing algorithm to establish a three-dimensional coordinate space of the target object; recognizing the cargo category of the recognition object based on the deep learning cognitive features of the images in the three-dimensional coordinate space.
Description
Technical Field
The invention belongs to the technical field of machine vision, and particularly relates to a self-service goods selling method for a container based on binocular vision.
Background
With the continuous acceleration of modern life rhythm, people put higher requirements on flexibility, convenience, quickness and the like of business models. In order to solve the increasingly outstanding demand, the concept of 'business automation' also appears, namely, the automation, informatization and mechanization of business operation are realized by using computer and mechanical technology. While a Vending Machine (drawing Machine) is a concrete embodiment of commercial automation. Although vending machines have been in the Chinese market for a long time, the development status is far from the foreign market. In developed countries such as the united states, japan, and europe, vending machines have been widely used as public facilities, for example, in schools, stations, offices, and apartments.
China cities are high in population density, and retail industries such as supermarkets and convenience stores are promoted to rapidly develop in the past few years. Nowadays, with the continuous rise of manpower cost, house renting cost and logistics cost, the retail industry has to face high operation cost. In addition, the network supermarkets such as a Tianmao supermarket, a first shop and the like rise rapidly, and the operation of the traditional retail industry is difficult. Compared with the prior art, the vending machine does not need to be operated by manpower, occupies small area, is flexible to place, reduces the operation cost, and provides all-weather convenient service for customers for 7 multiplied by 24 hours. Therefore, under the situation that the traditional retail industry faces operational dilemma, the development of the vending machine market will become the next trend.
However, the vending mode of the existing vending machine is more mechanical, the user is very inconvenient to take the goods, and the user experience is affected due to the fact that the goods are long in cost.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provide a binocular vision-based self-service goods selling method for a container, which has high identification accuracy and high speed.
In order to achieve the purpose, the invention adopts the following technical scheme:
a self-service goods selling method for a container based on binocular vision comprises the following steps:
shooting a first plane image and a second plane image of a target area of a vending machine or a sales counter through a binocular camera with fixed relative positions in the counter;
processing the first plane image and the second plane image by using a binocular vision processing algorithm to establish a three-dimensional coordinate space of the target object;
recognizing the cargo category of the recognition object based on the deep learning cognitive features of the images in the three-dimensional coordinate space.
Further, the processing the first planar image and the second planar image by using a binocular vision processing algorithm to establish a three-dimensional coordinate space of the target object includes:
s1: preprocessing the first plane image and the second plane image;
s2: intelligently recognizing the preprocessed first plane image and the preprocessed second plane image, determining generalized cognitive features before parallax calculation, and establishing a matching relation between the first plane image and the second plane image to identify cognitive attributes of the target area;
s3: confirming one or more specific cognitive characteristics corresponding to the cognitive attributes according to the cognitive attributes of the target area;
s4: performing parallax calculation according to a binocular stereo imaging principle;
s5: and establishing a three-dimensional coordinate space of the recognition object by combining the specific cognitive features and the point cloud picture.
The self-service goods selling method of the container further comprises the following steps:
s6: judging whether the identification degree of the three-dimensional coordinate space meets the requirements of precision and error; if yes, recognizing the cargo category of the recognition object based on the deep learning cognitive features of the image; if not, go to step S7;
s7: returning to step S2, the generalized cognitive characteristics are re-determined and execution continues at steps S3-S6.
Further, the step S4 includes acquiring a point cloud image of the target area.
Further, the generalized cognitive features comprise one or more of textures, outlines and colors; the specific cognitive features are contained within the generalized cognitive features; specific categories of the cognitive attributes include color, contour, surface texture, and geometry of the contour.
Further, the preprocessing in step S1 includes filtering, noise reduction, white balance, warping, and radial variation.
Further, the method for determining the generalized cognitive features before parallax calculation in step S2 includes: the method comprises the following steps of drawing type, geometric length of lines forming the drawing, colors of different characteristic regions forming the drawing, connection relation of the lines forming the drawing, geometric relation of the drawing and other generalized drawings, and length proportion relation of outlines forming the drawing.
Further, the self-service goods selling method for the container further comprises the following steps:
the method comprises the steps of collecting data of consumers by using a face recognition technology, wherein the data comprises age, sex, shopping time, place, variety and quantity of the consumers, and sending configuration information of the variety and quantity of goods to an owner through data collection and analysis of consumer behaviors. The operator can decide when to replenish goods or change goods according to the information, summarization and analysis of each terminal, thereby greatly improving the operation efficiency, and the careful plan can effectively reduce the inventory and logistics cost of the operator. On the other hand, the combination of the camera and the vending machine enables an operator to collect data of consumers through a face recognition technology, and through the collection of the data and the analysis of the behaviors of the consumers, the types and the quantity of goods of the vending machine can be configured more reasonably, a basis can be provided for product innovation, and the requirements of the consumers are better met.
The binocular vision-based self-service goods selling method for the container, provided by the invention, adopts a binocular vision-based identification method, accurately and efficiently identifies goods taken by a user, is an intelligent and informationized automatic goods selling operation management method, greatly improves the automatic goods selling operation efficiency, and has a very wide application prospect.
Drawings
FIG. 1 is a diagram illustrating a binocular vision based use scenario for self-service vending of containers in an embodiment of the present invention. Description of reference numerals: 10-binocular camera.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be obtained by a person skilled in the art without inventive effort based on the embodiments of the present invention, are within the scope of the present invention.
Referring to fig. 1, the binocular vision-based container self-service vending method according to one embodiment of the present invention includes the following steps:
shooting a first plane image and a second plane image of a target area of a vending machine or a sales counter through a binocular camera 10 with fixed relative positions in the counter;
processing the first plane image and the second plane image by using a binocular vision processing algorithm to establish a three-dimensional coordinate space of the target object;
recognizing the cargo category of the recognition object based on the deep learning cognitive features of the images in the three-dimensional coordinate space.
In a preferred embodiment, the processing the first planar image and the second planar image by using a binocular vision processing algorithm, and the establishing the three-dimensional coordinate space of the target object includes:
s1: preprocessing the first plane image and the second plane image;
s2: intelligently recognizing the preprocessed first plane image and the preprocessed second plane image, determining generalized cognitive features before parallax calculation, and establishing a matching relation between the first plane image and the second plane image to identify cognitive attributes of the target area;
s3: confirming one or more specific cognitive characteristics corresponding to the cognitive attributes according to the cognitive attributes of the target area;
s4: performing parallax calculation according to a binocular stereo imaging principle;
s5: and establishing a three-dimensional coordinate space of the recognition object by combining the specific cognitive features and the point cloud picture.
The self-service goods selling method of the container further comprises the following steps:
s6: judging whether the identification degree of the three-dimensional coordinate space meets the requirements of precision and error; if yes, recognizing the cargo category of the recognition object based on the deep learning cognitive features of the image; if not, go to step S7;
s7: returning to step S2, the generalized cognitive characteristics are re-determined and execution continues at steps S3-S6.
Further, the step S4 includes acquiring a point cloud image of the target area.
Further, the generalized cognitive features comprise one or more of textures, outlines and colors; the specific cognitive features are contained within the generalized cognitive features; specific categories of the cognitive attributes include color, contour, surface texture, and geometry of the contour.
Further, the preprocessing in step S1 includes filtering, noise reduction, white balance, warping, and radial variation.
Further, the method for determining the generalized cognitive features before parallax calculation in step S2 includes: the method comprises the following steps of drawing type, geometric length of lines forming the drawing, colors of different characteristic regions forming the drawing, connection relation of the lines forming the drawing, geometric relation of the drawing and other generalized drawings, and length proportion relation of outlines forming the drawing.
Further, the self-service goods selling method for the container further comprises the following steps:
the method comprises the steps of collecting data of consumers by using a face recognition technology, wherein the data comprises age, sex, shopping time, place, variety and quantity of the consumers, and sending configuration information of the variety and quantity of goods to an owner through data collection and analysis of consumer behaviors. The operator can decide when to replenish goods or change goods according to the information, summarization and analysis of each terminal, thereby greatly improving the operation efficiency, and the careful plan can effectively reduce the inventory and logistics cost of the operator. On the other hand, the combination of the camera and the vending machine enables an operator to collect data of consumers through a face recognition technology, and through the collection of the data and the analysis of the behaviors of the consumers, the types and the quantity of goods of the vending machine can be configured more reasonably, a basis can be provided for product innovation, and the requirements of the consumers are better met. Compared with the prior art, the method has the greatest innovation point that a technical means for identifying and positioning the target object is realized by adopting a mode of combining generalized cognitive features and specific cognitive features. Firstly, the generalized cognitive features comprise one or more of textures, outlines and colors; and the specific cognitive features are included within the generalized cognitive features. Specific categories of the cognitive attributes include color, contour, surface texture, and geometry of the contour. The specific cognitive features are deep learning cognitive features based on the images, and specific categories of the target areas are identified. Categories herein may include specific objects of various shapes, facial features, gestures, animal bodies, and so forth.
In conclusion, the binocular vision-based self-service goods selling method for the containers adopts the binocular vision-based identification method, accurately and efficiently identifies goods taken by the user, is an intelligent and informationized automatic goods selling operation management method, greatly improves the automatic goods selling operation efficiency, and has very wide application prospect.
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
Those of ordinary skill in the art will understand that: modules in the devices in the embodiments may be distributed in the devices in the embodiments according to the description of the embodiments, or may be located in one or more devices different from the embodiments with corresponding changes. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, but not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (3)
1. A self-service goods selling method for a container based on binocular vision is characterized by comprising the following steps:
shooting a first plane image and a second plane image of a target area of a vending machine or a sales counter through a binocular camera with fixed relative positions in the counter;
processing the first plane image and the second plane image by using a binocular vision processing algorithm to establish a three-dimensional coordinate space of a target object, comprising:
s1: preprocessing the first planar image and the second planar image;
s2: intelligently recognizing the preprocessed first plane image and the preprocessed second plane image, determining generalized cognitive features before parallax calculation, and establishing a matching relation between the first plane image and the second plane image to identify cognitive attributes of the target area;
the method for determining the generalized cognitive features before parallax calculation comprises the following steps: the method comprises the following steps of (1) determining the type of a graph, the geometric length of lines forming the graph, the color of different characteristic regions forming the graph, the connection relation of the lines forming the graph, the geometric relation of the graph and other generalized graphs, and the length proportional relation of outlines forming the graph;
s3: confirming one or more specific cognitive characteristics corresponding to the cognitive attributes according to the cognitive attributes of the target area;
s4: performing parallax calculation according to a binocular stereo imaging principle to obtain a point cloud picture of the target area;
s5: establishing a three-dimensional coordinate space of the recognition object by combining the specific cognitive features and the point cloud picture;
s6: judging whether the identification degree of the three-dimensional coordinate space meets the requirements of precision and error; if yes, then identifying the goods category of the identification object; if not, go to step S7;
s7: returning to the step S2, re-determining the generalized cognitive characteristics, and continuing to execute the steps S3-S6;
recognizing the cargo category of the recognition object based on the deep learning cognitive features of the images in the three-dimensional coordinate space;
wherein, the generalized cognitive features comprise one or more of texture, contour and color; the specific cognitive features are contained within the generalized cognitive features; specific categories of the cognitive attributes include color, contour, surface texture, and geometry of the contour.
2. The binocular vision based container self-service vending method of claim 1, wherein the preprocessing in the step S1 includes filtering, noise reduction, white balance, warping, and radial variation.
3. The binocular vision based container self-service vending method according to claim 1, further comprising the steps of:
the method comprises the steps of collecting data of consumers by using a face recognition technology, wherein the data comprises age, sex, shopping time, place, variety and quantity of the consumers, and sending configuration information of the variety and quantity of goods to an owner through data collection and analysis of consumer behaviors.
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| WO2019218303A1 (en) * | 2018-05-17 | 2019-11-21 | 深圳前海达闼云端智能科技有限公司 | Smart vending machine, object recognition method and device, server, and storage medium |
| CN108477937A (en) * | 2018-06-15 | 2018-09-04 | 芜湖启迪睿视信息技术有限公司 | A kind of sales counter monitoring system |
| CN109064636A (en) * | 2018-07-10 | 2018-12-21 | 安徽豆智智能装备制造有限公司 | A kind of purchase method of the automatic selling cabinet system based on image recognition |
| CN108937361B (en) * | 2018-07-13 | 2020-10-09 | 合肥美的智能科技有限公司 | Shelf and retail cabinet comprising same |
| CN109243110A (en) * | 2018-08-07 | 2019-01-18 | 北京云迹科技有限公司 | A kind of merchandise control method and counter |
| CN109974623B (en) * | 2018-09-13 | 2021-01-05 | 北京伟景智能科技有限公司 | Three-dimensional information acquisition method and device based on line laser and binocular vision |
| CN111380474A (en) * | 2018-12-29 | 2020-07-07 | 北京伟景智能科技有限公司 | Aperture detection method based on binocular vision |
| CN110473337A (en) * | 2019-08-01 | 2019-11-19 | 合肥世界仓库网科技有限公司 | A kind of automatic vending machine based on image collecting device |
| CN110610149B (en) * | 2019-09-03 | 2023-01-20 | 卓尔智联(武汉)研究院有限公司 | Information processing method and device and computer storage medium |
| CN112700587A (en) * | 2020-12-21 | 2021-04-23 | 湖南中谷科技股份有限公司 | Method for checking goods of vending machine and vending machine |
| CN113283848B (en) * | 2021-07-21 | 2021-09-28 | 湖北浩蓝智造科技有限公司 | Goods warehousing detection method, warehousing system and storage medium |
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