Human posture estimation method based on deformable convolutional neural network

Abstract

The invention relates to the technical field of depth learning and computer vision, in particular to a human posture estimation method based on a deformable convolutional neural network. The method comprises the following steps of 1, preprocessing an input dataset, wherein expanding and data set picture cutting are included; 2, performing K-means clustering on training set pictures; 3, adopting the K-means clustering result as a label, and inputting a training set into the deformable convolutional neural network to be trained; 4, constructing a score function F for training an SVM for joint classification; 5, performing outputting treatment. By reading the positive data sets, some data sets are adopted as the training set, some data sets are adopted as the validation set, negative data isread for comparison, and the negative data set is introduced for training, so that the background can be effectively filtered away to obtain interference, and the recognition rate is increased.

Description

technical field [0001] The invention relates to the technical fields of deep learning and computer vision, in particular to a method for estimating human body posture based on a deformable convolutional neural network. Background technique [0002] With the development of various online games and the popularity of animated videos, it has become a very hot topic to correctly and quickly recognize and understand the gestures of people in images; this problem is collectively called gesture detection. Pose detection contains many categories and sub-problems, and pose estimation is one of them; pose estimation is one of the most important computer vision challenges nowadays because it can be quickly applied to person tracking, action recognition, and video-related tasks. In video analysis, such as video surveillance and video search, etc.; the practical application is very wide. [0003] In the field of computer vision, the main task of pose estimation is, given a picture, there...

AI Technical Summary

Problems solved by technology

This type of method has a fast execution speed, does not need to retain a sample library, has a small storage cost, and does not require a special initialization process. The disadvantage is that the estimation accuracy is restricted by the size of the training sample. Due to the complexity of human body pose estimation, the existing deep learning technology It is impossible to effectively integrate deep learning and attitude expression information, and some complex attitudes cannot be effectively resolved; in order to solve the problem of low prediction accuracy in the existing technology, a new method needs to be proposed

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