Behavior monitoring method and apparatus

Abstract

The invention discloses a behavior monitoring method and apparatus. The method comprises the steps of triggering a second sensor to detect a first parameter in a first space when it is determined that an action of a to-be-monitored object is detected by a first sensor arranged in the first space, thereby obtaining a detection result of the first parameter; comparing the detection result of the first parameter obtained in a time period not shorter than a first set duration with a reference detection result, and when a comparison result represents that a fitting degree reaches a set threshold, determining that a first behavior of the to-be-monitored object occurs; and when it is determined that an occurrence duration of the first behavior of the to-be-monitored object is longer than a second set duration, outputting alarm information.

Description

technical field [0001] The invention relates to behavior monitoring technology, in particular to a behavior monitoring method and device. Background technique [0002] Existing human behavior recognition technologies are mostly implemented based on video surveillance technology. Data are collected through video equipment and image processing algorithms are used to analyze the data to identify user behavior. Recognizing user behavior through video will lead to insufficient protection of user privacy, complex image processing algorithms, and high hardware costs. To achieve detailed behavior recognition, a large number of video surveillance equipment needs to be deployed, and the recognition accuracy of video recognition user behavior is low. , the validity of the recognition result is easily affected by the structure of the house and the light in the house. [0003] In view of the above shortcomings, it is difficult to carry out large-scale promotion of video surveillance tec...

AI Technical Summary

Problems solved by technology

Recognizing user behavior through video will lead to insufficient protection of user privacy, complex image processing algorithms, and high hardware costs. To achieve detailed behavior recognition, a large number of video surveillance devices need to be deployed, and the recognition accuracy of video recognition user behavior is low. , the validity of the recognition results is easily affected by the structure of the house and the light inside the house

[0003] In view of the above shortcomings, it is difficult to carry out large-scale promotion of video surveillance technology in the application of real human behavior recognition

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