A Framework for Detecting Vehicle Occupancy Based on the Occupant Labeling Method

• Published in: Journal of advanced transportation Vol. 2020; no. 2020; pp. 1 - 8
• Main Authors: Lee, Jaeyun, Lim, Jaedeok, Byun, Jihye, Lee, Jooyoung
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 03.12.2020; Hindawi; Hindawi Limited; Wiley
• Subjects: Accuracy; Algorithms; Cameras; Detectors; High occupancy vehicle lanes; Human performance; Labeling; Labels; Methods; Model accuracy; Package design; Policies; Proposals; Tolls; Transportation; South Korea; United States > US
• ISSN: 0197-6729; 2042-3195
• DOI: 10.1155/2020/8870211

High-occupancy vehicle (HOV) lanes or congestion toll discount policies are in place to encourage multipassenger vehicles. However, vehicle occupancy detection, essential for implementing such policies, is based on a labor-intensive manual method. To solve this problem, several studies and some companies have tried to develop an automated detection system. Due to the difficulties of the image treatment process, those systems had limitations. This study overcomes these limits and proposes an overall framework for an algorithm that effectively detects occupants in vehicles using photographic data. Particularly, we apply a new data labeling method that enables highly accurate occupant detection even with a small amount of data. The new labeling method directly labels the number of occupants instead of performing face or human labeling. The human labeling, used in existing research, and occupant labeling, this study suggested, are compared to verify the contribution of this labeling method. As a result, the presented model’s detection accuracy is 99% for the binary case (2 or 3 occupants or not) and 91% for the counting case (the exact number of occupants), which is higher than the previously studied models’ accuracy. Basically, this system is developed for the two-sided camera, left and right, but only a single side, right, can detect the occupancy. The single side image accuracy is 99% for the binary case and 87% for the counting case. These rates of detection are also better than existing labeling.