School-bus-safety-oriented missed-child detecting method

Abstract

The invention relates to a school-bus-safety-oriented missed-child detecting method.Infrared sensor signals, video sensor signals and audio sensor signals are fused, the advanced video-analysis computer processing technology and the advanced audio-analysis computer processing technology are combined, and whether missed children exist on a school bus or not can be reliably detected.Meanwhile, reasonable power supply control is carried out through an ARM processing plate, power dissipation of a system is reduced, and the missed-child detecting method can be widely applied to the field of school-bus safety.The infrared signals, the video signals and the audio signals are fused, whether the missed children exist on the school bus or not can be reliably detected, power dissipation is low, installing is convenient and rapid, cost is low, and the missed-child detecting method can be widely applied to the field of school-bus safety.

Description

technical field [0001] The invention relates to a method for detecting missing children and belongs to the technical field of video surveillance. Background technique [0002] Missing children is a major hidden danger of school bus safety. In recent years, there have been many child deaths caused by missing children on school buses, which has aroused strong social concern. At present, it is mainly through strengthening ideological education and formulating more reasonable safety measures to avoid missing children as much as possible. For example, let the teacher who follows the bus go to the rear of the school bus to trigger the safety warning device after the students get off the bus, so that the teacher can carefully check whether there are missing children. However, manual inspection may cause accidents due to human negligence or carelessness, and there are potential safety hazards. In order to minimize the tragedy caused by missing children on the school bus, it is urg...

AI Technical Summary

Problems solved by technology

For example, in the field of human detection, there are currently many methods that use video analysis technology to detect human bodies. For example, JingShao et al. (Deeply Learned Attributes for Crowded Scene Understanding, CVPR2015) use deep learning methods to detect human targets in crowded scenes, but this method is not suitable for occluded human bodies on vehicles. The detection effect is not good; Seung-WookKim et al. (SensorFusion-BasedPeopleCountinggSystemUsingtheActiveAppearanceModels, ICCE2013) use the active shape model to detect the head area, but this method is mainly aimed at large places such as

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