Computer vision model for traffic sign recognition and detection—A survey

Abstract

Computer vision is an interdisciplinary field which deals with a high level understanding of digital videos or images. The result of computer vision is in the form of a decision or data. This also includes methods such as gaining, processing, analyzing, understanding, and extracting high dimensionality data. Object recognition is used for identifying the objects in any image or video. The appearance of objects may vary due to lighting or colors, viewing direction, and size or shape. The problem we identify here is accuracy at nighttime and in certain weather conditions is less that when compared to daytime and also we enable to detect some signs at the night time. In this paper, we present a detailed study of computer vision, object recognition, and also a study of traffic sign detection and recognition along with its applications, advantages, and disadvantages. The study focuses on several subject, e.g., proposal theme, model, performance evaluation, and advantages and disadvantages of the work. The performance evaluation part is further discussed w.r.t. the experimental setup, different existing techniques, and the various performance assessment factors used to justify the proposed model. This study will be useful for researchers looking to obtain substantial knowledge on the current status of traffic sign detection and recognition, and the various existing problems that need to be resolved.