Convolutional neutral network-based worker absence-from-post detection method

Abstract

The invention discloses a convolutional neutral network-based worker absence-from-post detection method. According to different characteristics extracted from monitoring images, the images are classified via an image classification algorithm, and whether workers are absent from posts can be detected via a convolutional neutral network training model. Monitoring workers in a company can be helped to process monitoring data efficiently via the worker absence-from-post detection method, whether the workers are at the posts can be determined, and whether the workers are absent from the posts can be accurately detected in working environment; a conventional convolutional neutral network structure can be improved so as to be applied to indoor working scenarios such as companies, enterprises and the like; worker absence-from-post detection can be realized, and the worker absence-from-post detection method is well applied to solving the problem.

Description

technical field [0001] The invention proposes a method for detecting personnel leaving work based on a convolutional neural network, which is a new application technology of the convolutional neural network for personnel detection. Background technique [0002] In recent years, with the continuous popularization of surveillance electronic equipment in various fields, a large number of surveillance videos and images need to be effectively processed. At the same time, companies (such as banks, stock exchanges, etc.) and business offices in the society need to ensure the arrival rate of employees due to business needs. In order to facilitate monitoring whether employees arrive on time and leave at will, it is necessary to use monitoring to understand , in order to have accurate attendance assessment for employees. The traditional monitoring method is manual monitoring, but the manual video monitoring method is inefficient and difficult to guarantee accuracy. Therefore, there i...

AI Technical Summary

Problems solved by technology

If the feature extraction is not accurate, the error will be larger and the detection will be inaccurate

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