Fall-down behavior real-time detection method based on depth image

Abstract

The invention discloses a fall-down behavior real-time detection method based on a depth image. The method comprises steps of depth image obtainment, human body image identification, depth difference feature extraction, human body part analysis, articulation point extraction, height feature extraction and fall-down behavior detection. Based on a depth image, a specific depth difference feature is selected on an identified human body image; a random forest classifier is used for analyzing human body parts; a human body is divided into a head part and a trunk part; articulation points are detected, and then a height feature vector is extracted; and a support vector machine classifier is used for detecting whether a detected object is in fall-down state. The invention provides the fall-down behavior detection method, improves the operation speed, and achieves timeliness of fall-down behavior detection. The depth image is utilized for fall-down behavior detection. On one hand, the method is free of influences of illumination and can be operated in an all-weather manner, and on the other hand, personal privacy can be protected compared with a colorful image. Only one depth sensor is required in terms of hardware support, and the advantage of low cost is achieved.

Description

technical field [0001] The invention belongs to the technical field of digital image recognition, and more specifically, relates to a real-time detection method of a fall behavior based on a depth image. Background technique [0002] With the serious aging of the social population, elderly care has gradually become a hot issue, and fall detection, as an important issue in elderly care, has gradually attracted people's attention. According to the different monitoring equipment and selection characteristics, the current fall detection system is mainly divided into three categories: fall detection system based on environmental monitoring, fall detection system based on wearable equipment and fall detection system based on video image. [0003] The environmental monitoring system has little impact on daily activities, but there are many sensors and the cost is high; the detection system of wearable devices has wide applicability and a small amount of calculation, but users need ...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the above defects or improvement needs of the prior art, the present invention provides a real-time detection method of falling behavior based on depth image, the purpose of which is to identify the falling behavior according

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