Comparison of Image Classification and Object Detection for Passenger Seat Belt Violation Detection Using NIR RGB Surveillance Camera Images

摘要

Manual seat belt enforcement by roadside police officers on highways is known to be inefficient, costly and ineffective. Authorities have been looking for automated enforcement solution towards seat belt violation detection. In this study, we examine different approaches towards the seat belt violation of front seat passengers using a near infrared (NIR) and color (RGB) surveillance camera system pointed at the vehicle's windshield. In these approaches an object detector; single shot multi box object detector (SSD) and image classifiers; convolutional neural network (CNN), Fisher vector (FV) are utilized to detect the usage of seat belt. Using a camera system placed on an overhead gantry installed on a highway, 13444 real world images are captured in a 20 hour time period. Our experiments conducted on this dataset show that SSD seat belt detector is superior for this problem with 91.9 % accuracy and 94.5 % precision rate.