Impact sound-based automatic traffic accident detection method

Abstract

The invention relates to an impact sound-based automatic traffic accident detection method. The method comprises the following steps of: framing an acquired sound signal, detecting the total energy distribution of a time domain, and performing further recognition if an impact condition is met and directly stepping out and waiting for the processing of a next frame if the impact condition is not met; judging the energy distribution of a frequency domain, extracting and judging an energy distribution characteristic component by using wavelets, performing further recognition if the impact condition is met and directly stepping out and waiting for the processing of the next frame if the impact condition is not met; and performing advanced communication function (ACF) detection, judging an impact sound if the impact condition is met or waiting for the processing of the next frame. The method has the advantages of improving detection accuracy, lowering misstatement rate and minimizing calculated amount.

Description

technical field [0001] The invention relates to the technical field of automatic detection of traffic accidents, in particular to a method for detecting traffic accidents by using sound signals. Background technique [0002] Compared with the development of automatic incident detection technology, the research on major traffic accident detection is lagging behind. Traditional traffic incident detection devices are mainly divided into vehicle detection equipment based on magnetic frequency signals, vehicle detection equipment based on spectral signals and vehicle detection equipment based on video signals. However, various sensors including cameras, ultrasonic waves, and microwaves are used to detect traffic incidents. They mainly focus on macroscopic road traffic flow information such as road flow, vehicle occupancy, and traffic density. The detection of microscopic phenomena such as traffic accidents is indirect. ,not effectively. At this stage, the most effective way to ...

AI Technical Summary

Problems solved by technology

[0005] Most of the existing detection algorithms based on audio signals are conducted offline experiments in a laboratory environment. For limited specific samples, a high detection rate and a low false positive rate have been achieved.

However, for the actual complex traffic environment, the sound signal changes in various ways, and the ideal effect cannot be achieved; some algorithms also have the disadvantage that the huge amount of calculation is not conducive to real-time detection

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