Bus crowdedness degree estimation method based on mobile phone sensor

Abstract

The invention relates to a bus crowdedness degree estimation method based on a mobile phone sensor. An own inertial sensor of a mobile phone is used, passenger movement gesture information identification on a bus is carried out under the frame of hierarchical learning after swarm intelligence perception thought is used for collecting data, and therefore, an accurate bus crowdedness degree estimation process is realized. According to the bus crowdedness degree estimation method, the own inertial sensor of the mobile phone can be used for collecting sensor data generated after a passenger gets on the bus until the passenger reaches a static state, the data can be used for identifying the passenger movement gesture information, and the crowdedness degree of the bus can be estimated through the information.

Description

technical field [0001] The present invention designs sensor applications, crowd perception, machine learning, action recognition, gait recognition, passive perception, layered learning, bus congestion estimation, etc. in computer science, especially a bus congestion sensor based on mobile phone degree estimation method. Background technique [0002] In recent years, as the sensor equipment equipped on mobile phones has become higher and higher, coupled with the continuous in-depth development of the field of machine learning, gesture recognition relies on fewer and fewer sensors, and at the same time, the accuracy of prediction is getting higher and higher. Relying on gesture recognition, we can achieve indoor positioning, daily sports data statistics, and can combine location information or communication signals to track a person's behavior. Predicting the degree of congestion of the bus is a relatively new application in the direction of action recognition. If the degree...

AI Technical Summary

Problems solved by technology

[0003] To obtain the degree of congestion in an area, it can be estimated by obtaining the total number of people in this area. The existing methods of counting people are all based on images or videos, which means that these methods rely on camera equipment, although these methods are in image Or under the premise that the video is clear, there is already enough accuracy, but the low-cost camera equipment cannot guarantee the clarity of the obtained image data in the case of poor light, and the high-cost camera equipment is obviously impossible

So people-based methods are not practical

The situation of people on the bus can also be counted by the data returned by the pressure sensor. This method will not be affected by environmental factors such as light and humidity, and the result is very accurate. However, if one of the sensors is broken, the number of people in the entire bus will The statistics will gradually tend to be too much or too little, and in the end there will be completely wrong results

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