|Table of Contents|

Algorithm for identifying violation behavior of vehicles of picking up passengers at airport departure floor based on surveillance video(PDF)

长安大学学报（自然科学版）[ISSN:1006-6977/CN:61-1281/TN]

Issue:
2022年4期
Page:
73-86
Research Field:
交通工程
Publishing date:

Info

Title:
Algorithm for identifying violation behavior of vehicles of picking up passengers at airport departure floor based on surveillance video
Author(s):
BAI Qiang1;  SHAO Yu-qi1;  MENG Si-yuan1;  WANG Yu-xuan1;  CHEN Xing2;  FENG Hong-xia3
(1. School of Transportation Engineering, Chang'an University, Xi'an 710064, Shaanxi, China; 2. Management Department of Kunming Changshui International Airport, Yunnan Airport Group, Kunming 650211, Yunnan, China; 3. College of Architecture, Xi'an University of Architecture and Technology, Xi'an 710055, Shaanxi, China)
Keywords:
traffic engineering;  violation identification algorithm;  target recognition and tracking;  violation pick-up;  airport landside;  YOLO_v4;  DeepSORT
PACS:
U491.4
DOI:
10.19721/j.cnki.1671-8879.2022.04.008
Abstract:
To effectively monitor the violation behavior of vehicles that pick up passengers at the departure floor of airports, and reduce the impact of such violation behavior on the curbside traffic capacity, a method based on YOLO_v4 & DeepSORT algorithm was proposed, to automatically identify vehicles that pick up passengers at the departure floor based on surveillance video. Firstly, YOLO_v4 was used to detect the targets, the displacement of vehicle targets in each running state was recorded, and the threshold value of the vehicle targets' running state was analyzed. Based on the fixed monitoring position, a vehicle motion state detection algorithm was developed. Then, based on the identification and tracking information obtained by YOLO_v4 & DeepSORT, an identification algorithm for pick-up and drop-off behaviors was established. The algorithm used YOLO_v4 & DeepSORT to identify the target and to record and process the classification and location information. After the running state of the vehicle and the related behaviors of the driver and passenger near the vehicle were identified, the passenger behavior information could be recorded when the vehicle stops, and the change in the number of people in the vehicle could be calculated. After the vehicle disappears from the monitoring area, the pick-up behavior of the vehicle couldbe identified based on the final change in the number of passengers in the vehicle. Finally, the algorithm was programmed by Python and tested using the surveillance video of Kunming Changshui International Airport. The results show that the proposed algorithm can effectively identify the pick-up behavior from the departure floor of the airport, and the identification accuracy of the algorithm reaches 86.49%, which can provide an effective way to identify the violation pick-up behavior at the departure floor. At the same time, only 0.41% of vehicles are misidentified, which shows a low misidentification rate, indicating that the algorithm is helpful to differentiate normal vehicles and violation vehicles among all the vehicles on the departure floor.6 tabs, 12 figs, 24 refs.

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Last Update: 2022-07-20