Automatic sample key point labeling method, device and system

Abstract

The invention provides an automatic sample key point labeling method, device and system, and the method comprises the following steps: receiving a real-time image, and extracting SIFT features of thereal-time image; calculating a matching point according to the SIFT features of a target template image and the SIFT features of the real-time image; calculating a transmission transformation matrix from the target template image to the real-time image according to the matching point; calculating the positions of key point positions in the target template image in the real-time image according tothe transmission transformation matrix; and storing the real-time image and the corresponding key point positions as sample data. According to the method, an existing template matching technology is utilized, targets and corresponding key points under different backgrounds are matched in real time, and high-quality image-key point sample pairs are acquired in batches. Compared with a traditional manual sample labeling method, the method is higher in efficiency and better in universality.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to an automatic sample key point labeling method, device and system. Background technique [0002] The current deep learning technology has greatly improved the level of object detection and classification in the industry. The current mainstream deep learning technology requires a large number of labeled samples for training, and sample labeling requires manual labeling. Especially in the fields of key point detection and semantic segmentation, manual annotation is inefficient and expensive. And in actual production applications, there is a great demand for key point detection of specific objects, but due to the small number of labeled samples, the existing deep network cannot be trained well and cannot achieve the expected goal. [0003] At present, deep neural networks can achieve accuracy comparable to humans under a large amount of training data, but the training o...

AI Technical Summary

Problems solved by technology

[0004] The present invention proposes an automatic key point labeling technology. The key points are marked on the target template in advance, and the target pictures in different backgrounds in the camera screen are automatically superimposed and marked through the feature point matching method to generate a large number of image-label pairs, which can effectively Solve the problem of insufficient training samples for deep neural network models

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