The Architecture of the Blind Assistant System for Passing Intersection by Mobile Cloud Computing

Subject: 

Today, with the advancement of technologies to assist blind and visually impaired people, navigation systems are of great importance. As a result of emerging technologies in telecommunication and smartphones, these people can be helped. Identifying pedestrian and traffic lights is important to help pedestrians with visual impairments cross the intersection safely and securely. Background- researchers have studied the detection and identification of traffic lights in the assistive system or blind assist device. These researches can be divided into three main types: based on pattern matching, based on circular shape extraction, and based on color distribution.

In this research, an architecture based on mobile cloud computing is proposed, which can help blind pedestrians in crossing intersections. The architecture consists of three tiers: mobile phone, cloud, and supervision. The most important component is located on the mobile phone. It recognizes the color of pedestrian light by using image processing techniques. Spatial information (time and location) of the blind person is collected and held in a cloud storage database so that acquaintances can monitor him if needed. In order to detect the status of pedestrian lights, pictures of crossing streets with cameras will be captured. Using the features of color and morphology operations, the color of pedestrian lights is recognized and reported to the blind person. To this end, morphological operations are performed to eliminate small elements in the background and to restore the original size of the traffic light sign. Therefore, the operations of dilation, filling, and erosion are used.

We gathered a dataset including 280 photos of pedestrian lights (170 photos at day, 110 photos at night) in different illumination conditions (early day, noon, early night, night) and weather (sunny, cloudy, rainy). Matlab software and notebook system with Intel (R) Core (TM) i5 CPU and AMD Mobility Radeon HD 5100 graphics card were used to implement pedestrian traffic-light status detection. The scenario-based method is used to evaluate the system architecture and show that the proposed system can satisfy the investigated scenario. At last, the implementation results on taken images show excellent performance in detecting pedestrian lights with approximately 100% accuracy for the day and night.