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%.

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.

In this paper, we develop an Intelligent Wheelchair (IW) system for the severely disabled people. The main function of our system is two-fold: 1) user intention recognition using vision technique and 2) sensor-based obstacle detection and path finding. The proposed system enable a user to control IW using his mouth shape and face movement. Furthermore, to fully guarantee user's safety, the 10 range sensors are used to detect obstacles in environment and avoid them. To assess the effectiveness of the proposed IW, it was tested with 34 users and then the results show that it can provide a user unable to drive a standard joystick with friendly and convenient system.

The selection of canonical images that best represent a scene type is very important for efficiently visualizing search results and re-ranking them. The canonical images can be obtained using various aspects including viewpoint, visual features, and semantics. Here, we propose the selection of canonical images based on human affects. The proposed method is performed using three steps: extract the affective features from the input image, cluster images in the affective space and rank the clusters, and find representative images within each cluster. First, the probabilistic affective model is used to transform the images into the affective space. Thereafter, the images are clustered in the affective space. Then, the selected canonical images are representative and distinctive from each other. Thus, we define three prominent properties that an informative summary should satisfy: coverage, affective coherence, and distinctiveness. Based on these, cluster ranking is performed. Finally, the representative images for each cluster are selected, all of which are displayed as canonical images to the user. Experiments using web image databases demonstrate are not only representative but also exhibit a diverse set of views with minimal redundancy.