Tumble detection method based on video articulation points and hybrid classifier

Abstract

The invention discloses a tumble detection method based on video articulation points and a hybrid classifier. A traditional video-based fall detection algorithm depends on manual extraction of fall features, fall is detected by means of a linear discriminant classifier method, the model is simple, but the accuracy is low. The method comprises the following steps: 1, extracting each frame of imageof a detected video clip; 2, acquiring a human joint data matrix; 3, establishing a plurality of behavior matrixes; and 4, calculating a time characteristic parameter and a space characteristic parameter; 5, carrying out primary classification; 6, carrying out secondary classification. According to the method for extracting the human skeleton joint points, the problem that the human posture cannotbe accurately estimated by extracting the human aspect ratio, the projection area and the like through a traditional method is solved. The behavior matrix is constructed by adopting the sliding window with the fixed size, modeling can be performed on time and space axes at the same time, and falling behavior characteristics are fully expressed.

Description

technical field [0001] The invention belongs to the technical field of fall detection, in particular to a fall detection method based on video joints and hybrid classifiers. Background technique [0002] Scholars at home and abroad have done a lot of research on the fall of the elderly. There are three mainstream fall detection methods: fall detection based on wearable sensors, fall detection based on environmental sensors, and detection based on video images. The fall detection based on wearable sensors mainly collects the data collected by wearable sensors to set the threshold to detect falls, and the inaccurate threshold setting will affect the final detection results. The fall detection based on the environmental sensor mainly judges the fall through the data obtained by the pressure sensor on the ground or the sound detection equipment. If the environmental noise is too large, the data may be abnormal. Video-based fall detection mainly uses ordinary cameras or depth ca...

AI Technical Summary

Problems solved by technology

However, the traditional video-based fall detection algorithm relies on manually extracting fall features, and detects falls with the help of a linear discriminant classifier. The model is simple but the accuracy is low. The existing deep learning-based detection algorithm model is complex, and it is difficult to reduce the time-consuming detection.

Images