Falling-into-water detection and alarm system

Abstract

The invention discloses a falling-into-water detection and alarm system. The system comprises high-performance embedded GPU computing equipment integrated with a video acquisition module, a personnelfalling-into-water detection module and an alarm module. All the modules are operated on the embedded GPU computing equipment; the video acquisition module carries out decoding and image conversion ona real-time video stream shot by the front-end camera, thus video image data which can be calculated is obtained; during detection, firstly, video frame data of a camera at the front end of a monitored water area is collected in real time through a video collection module; the personnel falling-into-water detection module defines a detection area for the video frame data, then performs personneldetection on the video frame data in the defined detection area by utilizing the personnel falling-into-water detection module, sends an alarm signal in time once finding out a personnel falling-into-water situation, and sends personnel falling-into-water information to the background server. The method is used for realizing all-weather 24-hour automatic real-time early warning of personnel falling into water, manpower and material resources are effectively saved, the accuracy is improved, and the method can be deployed at the front end and is convenient and fast.

Description

Technical field [0001] The invention relates to the technical field of video intelligent monitoring, in particular to a falling water detection and alarm system. Background technique [0002] There are a huge number of artificial lakes, reservoirs and lakes in parks developed by our country. In actual life, safety accidents occur frequently due to inadequate safety measures, ineffective inspections, and ineffective monitoring. Therefore, it is particularly important to be able to find the person who fell into the water and implement rescue at the first time. [0003] At present, the methods for detecting people falling into the water are mainly based on traditional image processing algorithms. Such algorithms are greatly affected by factors such as light, complex background, and large fluctuations in the lake surface, and are prone to many false alarms; some use deep learning methods for falling people Detection can effectively eliminate the interference caused by factors such as ...

AI Technical Summary

Problems solved by technology

[0003] At present, the methods for detecting people in the water are mainly based on traditional image processing algorithms. Such algorithms are greatly affected by factors such as light, complex background, and large fluctuations in the lake surface, and are prone to many false positives; some of them use deep learning methods to detect people in the water. Detection can effectively eliminate the interference caused by complex background, light and other factors, but this method only collects samples of people who fell into the water for model training. Due to the different postures of people in complex scenes, it is easy to cause interference and false alarms

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