Attitude detection method using deep convolutional neural network and equipment

Abstract

The invention discloses an attitude detection method using a deep convolutional neural network, and is suitable for being performed in computing equipment. The method comprises the steps that a data set is divided into training and testing and preprocessed; identification learning model training of human body joint feature areas is performed so as to identify the learning network of human body joint image areas; joint coordinate positioning learning model training is performed; detection image size preprocessing is performed, and the images requiring human body attitude identification are adjusted into the size of the network input requirements; identification of the image joint areas is performed through the network and corresponding rectangular areas are defined to be saved as sub-images; the acquired sub-images act as the input to be transmitted to the joint coordinate positioning learning model to perform joint coordinate acquisition; and the acquired joint points are connected according to the human skeleton model so as to form human body attitude description. The invention also provides storage equipment and a mobile terminal.

Description

technical field [0001] The invention belongs to a posture detection method, in particular to a posture detection method and equipment using a deep convolutional neural network. Background technique [0002] Human motion and posture capture has broad application prospects in the fields of auxiliary clinical diagnosis, rehabilitation engineering, human motion analysis, intelligent human-computer interaction and intelligent monitoring, and is an important topic in the field of machine vision. Human body posture recognition based on machine vision refers to finding and extracting human body action features from video image sequences, and then matching and classifying three-dimensional human body capture data to determine action parameters has become a new way to break through this constraint. However, the current motion capture equipment is expensive, difficult to operate, and weak in data reprocessing performance. [0003] In the field of intelligent monitoring, at present, th...

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Problems solved by technology

[0005] Aiming at the technical problems existing in the prior art, the present invention provides a human body posture detection based on a deep convolutional neural network that can overcome the deficiencies of the above-mentioned prior art (poor robustness, poor anti-self-occlusion ability, high training requirements, etc.) Methods and equipment

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