Image stitching and deduplication method and device based on video frame extraction

Abstract

The invention discloses an image stitching and deduplication method based on video frame extraction. The method comprises the following steps: acquiring video data of a target area, and extracting frames from the video data to obtain a key frame image set of a group of video data; inputting the key frame image set into a preset stitching library to obtain a panoramic image corresponding to the keyframe set and a homography transformation matrix of each key frame image in the key frame set; inputting the key frame set into a preset target detection model to obtain a target detection result ofeach key frame image in the key frame image set; and performing coordinate transformation and deduplication on the target detection result of each key frame image in the key frame image set, and outputting the panoramic image and the target detection deduplication result of the panoramic image. According to the invention, the manpower consumption for shooting a plurality of scene photos can be avoided by automatically extracting the key frame, and meanwhile, the time and the manpower cost can be saved and the identification efficiency and the identification precision can be improved by identifying the key frame and automatically de-duplicating the identification result.

Description

technical field [0001] The present invention relates to the technical field of computer vision and machine learning, in particular to an image splicing and deduplication method, device and computer-readable storage medium based on video frame extraction. Background technique [0002] In real life, panoramic image stitching and target detection deduplication play a vital role in object recognition and scene understanding in large scenes. It is widely used in robot navigation, intelligent traffic monitoring, industrial inspection, aerospace, etc. In the field, this technology can reduce the consumption of labor costs, which has important practical significance. [0003] At present, there are mainly two types of commonly used scene target detection. The first one is to identify by taking multiple scene photos, and then count the recognition results of all photos, manually deduplicate the repeatedly recognized targets in the photos, and finally get the whole scene. object ident...

AI Technical Summary

Problems solved by technology

Although this method can avoid manual deduplication of the recognition results, since the entire scene image obtained by splicing often has a large resolution, the target detection model needs to consume more time when recognizing objects in large-resolution images. Hardware resources and processing time. At the same time, this kind of splicing recognition has high requirements for shooting each image. It is necessary to check the overlapping area and shooting angle of the two before and after each photo when shooting, which increases the labor cost.

In addition, although reducing the recognition of large-resolution images can reduce the consumption of hardware resources, it can easily lead to a decrease in the accuracy of object recognition. Therefore, this method often fails to achieve the best balance between resource consumption, time efficiency and recognition accuracy. its use is limited

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