Wi-Fi-based recognition method and system for on-duty behavior of ship bridge personnel
A recognition method and cab technology, applied in the field of pattern recognition, can solve the problems of infringing on the privacy of cab personnel and poor monitoring of cab personnel's on-duty behavior, and achieve the effect of protecting personal privacy and avoiding privacy
Active Publication Date: 2020-11-13
WUHAN UNIV OF TECH
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Problems solved by technology
[0004] The purpose of the present invention is to overcome the defects and problems existing in the prior art that the monitoring effect of the on-duty behavior of the cab personnel is poor and the privacy of the cab personnel is violated, and to provide an all-weather recognition of the on-duty behavior of the cab personnel without dead ends , and avoid infringing the privacy of the bridge personnel, a WiFi-based method and system for identifying the on-duty behavior of the personnel in the bridge of the ship
Method used
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[0097] see figure 1 , a kind of WiFi-based ship bridge personnel on-duty behavior recognition method, the method comprises the following steps:
[0098] A. Deploy equipment, use laptops, commercial WiFi equipment and Inter5300 network cards to collect CSI signals in the entire ship's cab; specifically include the following steps:
[0099] A1. Collect the signal in the static state of the ship's bridge as the reference data;
[0100] A2. Collect the signal of the human body when walking around the device, sitting and moving in situ as dynamic data;
[0101] The channel frequency response of the acquired signal is expressed as:
[0102]
[0103] In the above formula, H(f k ) is the center frequency f k The CSI information of the kth subcarrier, ||H(f k )|| is the amplitude information of the subcarrier, ∠H(f k ) is the phase information of the subcarrier;
[0104] B. Filter the collected CSI signal to eliminate the noise caused by the ship's steel environment and dynam...
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A WiFi-based identification method for on-duty behavior of ship bridge personnel, the method comprising the following steps: A, deploying equipment to collect CSI signals in the entire ship bridge; B, filtering the collected CSI signals; C, filtering the filtered CSI signal is multipath removed; D. Use principal component analysis to reduce the dimensionality of the CSI signal after multipath removal; E. Extract a continuous CSI signal in a sliding window W, analyze its time domain and frequency domain information, and calculate Statistical characteristics of each behavior under this information; F. For the calculated behavioral statistical characteristics, use the hidden Markov model to classify and identify its state behavior; G. Build an abnormal state detector to detect the cab personnel on duty Abnormal state behavior. This design can not only realize all-weather recognition of the on-duty behavior of the cab personnel without dead angle, but also avoids infringing on the privacy of the cab personnel.
Description
technical field [0001] The present invention relates to the technical field of pattern recognition, in particular to a WiFi-based method and system for identifying on-duty behaviors of ship cab personnel, and is mainly suitable for realizing all-weather identification of the on-duty behavior of the cab personnel without dead ends, and avoiding violations of the cab personnel privacy. Background technique [0002] The safety of ship navigation has always been a hot issue that people pay attention to. Studies have shown that many navigation accidents are caused by ship drivers who did not conscientiously fulfill the rules of the driver's duty, fatigue driving, and negligent driving. Therefore, it is necessary to take some measures to regulate the duty behavior of ship pilots. [0003] Most of the existing methods are to deploy surveillance cameras using computer vision, but this method has several disadvantages: first, surveillance cameras cannot work effectively in the dark ...
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IPC IPC(8): H04W4/021H04W4/33H04W4/30H04W24/08G06K9/62G06K9/00
CPCH04W4/021H04W4/33H04W4/30H04W24/08G06F2218/08G06F2218/12G06F18/2135G06F18/2415
Inventor 刘克中陈嘉鸣陈默子曾旭明马杰王国宇马玉亭李春伸
Owner WUHAN UNIV OF TECH