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

Abstract

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.

Passenger Occupancy Estimation in Vehicles: A Review of Current Methods and Research Challenges

“…This classifier scored below 50% in experiments. In 2020, Lee et al proposed a system for the two-sided camera with only the right side capable of detecting the occupancy in paper [64]. For the binary case in HOV lanes, the system achieved an accuracy of 99% and for detection-87%.…”

Review of IoT Sensor Systems Used for Monitoring the Road Infrastructure

“…There are two options for checking the passenger count in the vehicles. The first one is using sensors for person detection from the checking station [61]. The second one is using seat occupancy sensors installed on the car's interior [62].…”

TICaM: A Time-of-flight In-car Cabin Monitoring Dataset

“…As mentioned in section 1, occupant classification is a safety-critical task for safe deployment of airbags. Many past works have addressed this problem but no dataset has been made publicly available [17,19,23,13]. Nowruzi et al [21] released a dataset of thermal images for occupant classification, however, their dataset lacks images captured with child seats and children/infants in the scene.…”