Abstract
To ensure the public’s safety such as in buses, it is very important to accurately judge people’s behaviors and give early warnings. If by watching the video surveillance manually, the cost will be very high, and it cannot be effectively popularized, so video automatic monitoring is preferred. For buses, its environmental space is closed as well as narrow, and at the same time, it is often in a non-stationary state, so traditional behavior detection methods cannot be used here as they are easily affected by moving environment and difficult to fulfill object behavior identification in real time. Aiming at this problem, for people’s fast-moving in buses, a kind of detection method based on YOLOv5 is proposed in this paper. Firstly, the method detects people through one-stage object detection. Secondly, in order to obtain the person's movement trajectory quickly and accurately, an improved two-stage object matching algorithm is designed to track different people. Then, the speed curves of a person during normal activities and fast moving are compared. Finally, an abnormal alarm mechanism is constructed to realize the effective fast movement alarm. Surveillance video in the bus was used to test and evaluate the effectiveness of the method. Results show that the accuracy rate of our method can get 95.4%.
Highlights
People's safety in public places is very worthy of attention
Intelligent video surveillance system has been widely used in various public places, such as banks, hospitals, campuses [1,2,3]
In order to solve the problems of behavior recognition in such special scenes, this paper proposes a new kind of people’s fast moving detection method in buses based on YOLOv5
Summary
People's safety in public places is very worthy of attention. For this reason, intelligent video surveillance system has been widely used in various public places, such as banks, hospitals, campuses [1,2,3]. Frame difference and background subtraction have simple principle and good detection effect in most scenes, they are affected by light, cameras’ movement and change of International Journal of Sensors and Sensor Networks 2021; 9(1): 30-37 background These methods are not suitable for moving scenes as in buses. Based on the deep learning model and the tracking strategy, a two-stage object matching algorithm is constructed, which combines the advantages of different algorithms to meet the application requirements [9,10,11] In this part, the bounding box obtained by the detection algorithm is matched to achieve multi-object tracking, so as to accurately obtain the real-time changes of people’s position. The method in this paper analyzes and measures the internal spatial structure of the bus, obtains the relationship between the image pixel and the actual distance, calculates the moving speed of the people objects, sets the trigger alarm mechanism, and outputs alarm signals
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