Commodity placement position panorama generation method based on video target tracking

Abstract

The invention discloses a commodity placement position panorama generation method and device based on video target tracking, and the method and device can achieve the target detection, tracking and recognition of a commodity position in a video based on deep learning. By utilizing the characteristics that the relative position of the static target is invariant and the tracking target correspondingto the same commodity can appear in multiple video frames of the video, the accuracy of commodity tracking target position and category identification is improved, so that the image quality requirement of the commodity in each video frame is reduced, and the efficiency and quality of panoramic image synthesis are improved. When a video is shot, video shooting conditions do not need to be strictlyrequired, for example, the video can be shot from up, down, left and right directions, so that the requirement on video shooting is reduced, and the video data acquisition efficiency before panoramicpicture synthesis is improved. The generated graph can select to use a two-dimensional graph or a three-dimensional graph as required, so that a panoramic graph with more diversified display forms can be generated.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to a method and device for generating panorama graphics of commodity placement positions based on video target tracking. Background technique [0002] In the FMCG industry, the display position of products in retail channels is directly related to their sales. Therefore, brand owners have a strong demand for the digitization of product display, especially beverage brand owners want to know the position of products on their own shelves or on customer shelves. Display location, by analyzing a large number of terminal product display data, to understand the advantages and disadvantages of this product and competing products. [0003] In the prior art, in order to obtain the display position of each commodity on the shelf, it is generally possible to obtain the display situation of the commodity on the shelf by taking pictures. However, for scenes with long shelves or ...

AI Technical Summary

Problems solved by technology

However, for scenes with long shelves or large display space, it is impossible to obtain the status of product display by taking a picture. At this time, it is necessary to take a picture stitching method to obtain a panoramic image of the product display position on the shelf.

However, the photo stitching method has high requirements for shooting, and the usability of each picture needs to be checked after it is taken, which reduces the efficiency of synthesizing panoramic images

At the same time, once there are problems such as image distortion and ghosting in the pictures used for stitching, it will seriously affect the stitching effect of the panoramic image, and even the stitching cannot be completed

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