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Research in social science has shown that the mobile phone users pay less attention to their surroundings, which exposes them to various hazards such as collisions with vehicles than other pedestrians. In this paper, we propose a novel handheld device that assists mobile phone users to walk more safely outdoors. The proposed system is implemented on a smart phone and uses its back camera to detect the current outdoor context, e.g. traffic intersections, roadways, and sidewalks, finally alerts the user of unsafe situations using sound and vibration from the phone. The outdoor context awareness is performed by three steps: preprocessing, feature extraction, and context recognition. First, it improves the image contrast while removing image noise, and then it extracts the color and texture descriptors from each pixel. Next, each pixel is classified as an intersection, sidewalk, or roadway using a support vector machine-based classifier. Then, to support the real-time performance on the smart phone, a multi-scale classification is applied to input image, where the coarse layer first discriminates the boundary pixels from the background and the fine layer categorizes the boundary pixels as sidewalk, roadway, or intersection. In order to demonstrate the effectiveness of the proposed method, some real-world experiments were performed, then the results showed that the proposed system has the accuracy of above 98% at the various environments.

Outdoor Context Awareness Device That Enables Mobile Phone Users to Walk Safely through Urban Intersections