Vehicle Count System based on Time Interval Image Capture Method and Deep Learning Mask R-CNN
Eduardo Piedad, Tuan-Tang Le, Kimberly Aying, Fhenyl Kristel Pama, Ianny Tabale · 2019
Traffic congestion is an undesirable problem for big cities especially in third world countries. Better policy planning and decision-making from the authority comes from well-conducted practical research. In this study, a Vehicle Count System (VCS) using deep learning Mask R-CNN is developed to classify and count vehicles passing in a target street. A novel time interval image capture (TIIC) system is employed to the VCS instead of the typical real-time video streaming to avoid big data storage cost. To determine the effectiveness of the developed VCS, its output is compared to that of conventional method from manual recording of the passing vehicles. Four vehicle types - cars, motorbikes, trucks and buses are present in the 1800 real traffic images gathered from an actual field. As an initial stage, the developed tool performs satisfactorily in classifying and counting car-type vehicle with 97.62% accuracy in a 10-hour testing. However, it fails to recognize motorbikes probably due to its relatively smaller pixel size compared to other vehicle types. The presence of jeeps confused the VCS. The real image dataset can be used as basis for further development. The newly developed TIIC system can also be used in future research as a promising tool to replace real-time video streaming.