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Audio and video combined pedestrian accidental tumble monitoring method based on neural network

A neural network, audio and video technology, applied in the field of computer vision, can solve the problem of insufficient fall recognition, achieve the effect of improving computing efficiency, shortening computing time, and meeting test requirements

Pending Publication Date: 2022-01-04
黑龙江雨谷科技有限公司
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AI Technical Summary

Problems solved by technology

[0003] At present, the main research on fall recognition is focused on the fall recognition in the indoor environment, but there is still not much research on the fall recognition in the outdoor environment.

Method used

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  • Audio and video combined pedestrian accidental tumble monitoring method based on neural network
  • Audio and video combined pedestrian accidental tumble monitoring method based on neural network
  • Audio and video combined pedestrian accidental tumble monitoring method based on neural network

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Embodiment Construction

[0074] The principles and features of the present invention are described below in conjunction with the accompanying drawings, and the examples given are only used to explain the present invention, and are not intended to limit the scope of the present invention.

[0075] The present invention provides a neural network-based audio-video combined pedestrian accidental fall monitoring method. In order to make the present invention more obvious and understandable, the present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0076] The concepts involved in the embodiments of the present disclosure are introduced below. Faster RCNN is an improvement on the basis of R-CNN and Fast RCNN. The Faster RCNN framework mainly includes three parts: basic feature extraction network, region proposal network, and FastRCNN. It is suggested that the network and Fast RCNN share the convolutional feature extraction network...

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Abstract

The invention provides an audio and video combined pedestrian accidental tumble monitoring method based on a neural network. The method comprises the steps of constructing an initial training set and a test set; constructing a pedestrian target detection model; carrying out time sequence classification recombination on the pedestrian targets output by the pedestrian target detection model by using a frame recombination method; constructing an action recognition classification network model as a video classifier; constructing a call-for-help sound classification network model as an audio classifier; performing joint judgment on video and audio classification results by using a D-S evidence theory; and comprehensively outputting a judgment result. According to the invention, the tumble accident of the pedestrian is monitored by using the audio and video monitoring data, assistance is provided for public safety behavior analysis, and the loss of life and property is reduced.

Description

technical field [0001] The invention relates to the technical field of computer vision, in particular to a method for monitoring accidental falls of pedestrians based on neural network combined with audio and video. Background technique [0002] The health of the elderly is widely concerned. Due to the degradation of physiological function, the change of psychological state and the weakening of social function, the elderly are very prone to falls in daily life. According to the 2015 China Death Cause Survey, falls accounted for the largest proportion of injuries among the elderly over 65 years old, and falls are an important cause of disability and death in the elderly. [0003] At present, the main research on fall recognition focuses on fall recognition in indoor environments, but there are still not many studies on fall recognition in outdoor environments. In China, it is not uncommon for the elderly to fall down outdoors and get injured or even die because no one finds...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/047G06N3/048G06N3/045G06F2218/04G06F2218/12G06F2218/08G06F18/241G06F18/2415G06F18/254
Inventor 国强王亚妮王文博戚连刚
Owner 黑龙江雨谷科技有限公司
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