Recently, the correct estimation of traffic flow has begun to be considered an essential component in intelligent transportation systems. In this paper, a new statistical method to predict traffic flows using time series analyses and geometric correlations is proposed. The novelty of the proposed method is two-fold: (1) a 3D heat map is designed to describe the traffic conditions between roads, which can effectively represent the correlations between spatially- and temporally-adjacent traffic states; and (2) the relationship between the adjacent roads on the spatiotemporal domain is represented by cliques in MRF and the clique parameters are obtained by example-based learning. In order to assess the validity of the proposed method, it is tested using data from expressway traffic that are provided by the Korean Expressway Corporation, and the performance of the proposed method is compared with existing approaches. The results demonstrate that the proposed method can predict traffic conditions with an accuracy of 85%, and this accuracy can be improved further.
A significant challenge faced by visually impaired people is ‘wayfinding’, which is the ability to find one’s way to a destination in an unfamiliar environment. This study develops a novel wayfinding system for smartphones that can automatically recognize the situation and scene objects in real time. Through analyzing streaming images, the proposed system first classifies the current situation of a user in terms of their location. Next, based on the current situation, only the necessary context objects are found and interpreted using computer vision techniques. It estimates the motions of the user with two inertial sensors and records the trajectories of the user toward the destination, which are also used as a guide for the return route after reaching the destination. To efficiently convey the recognized results using an auditory interface, activity-based instructions are generated that guide the user in a series of movements along a route. To assess the effectiveness of the proposed system, experiments were conducted in several indoor environments: the sit in which the situation awareness accuracy was 90% and the object detection false alarm rate was 0.016. In addition, our field test results demonstrate that users can locate their paths with an accuracy of 97%.
This paper presents an indoor wayfinding system to help the visually impaired finding their way to a given destination in an unfamiliar environment. The main novelty is the use of the user"s situation as the basis for designing color codes to explain the environmental information and for developing the wayfinding system to detect and recognize such color codes. Actually, people would require different information according to their situations. Therefore, situation-based color codes are designed, including location-specific codes and guide codes. These color codes are affixed in certain locations to provide information to the visually impaired, and their location and meaning are then recognized using the proposed wayfinding system. Consisting of three steps, the proposed wayfinding system first recognizes the current situation using a vocabulary tree that is built on the shape properties of images taken of various situations. Next, it detects and recognizes the necessary codes according to the current situation, based on color and edge information. Finally, it provides the user with environmental information and their path through an auditory interface. To assess the validity of the proposed wayfinding system, we have conducted field test with four visually impaired, then the results showed that they can find the optimal path in real-time with an accuracy of 95%.
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