A Crowdsourcing Approach to Promote Safe Walking for Visually Impaired People

[[abstract]]Visually impaired people have difficulty in walking freely because of the obstacles or the stairways along their walking paths, which can lead to accidental falls. Many researchers have devoted to promoting safe walking for visually impaired people by using smartphones and computer vision. In this research we propose an alternative approach to achieve the same goal - we take advantage of the power of crowdsourcing with machine learning. Specifically, by using smartphones carried by a vast amount of visually normal people, we can collect the tri-axial accelerometer data along with the corresponding GPS coordinates in large geographic areas. Then, machine learning techniques are used to analyze the data, turning them into a special topographic map in which the regions of outdoor stairways are marked. With the map installed in the smartphones carried by the visually impaired people, the Android App we developed can monitor their current outdoor locations and then enable an acoustic alert whey they are getting close to the stairways.[[notice]]補正完

Interactive visualization of high density streaming points with heat-map

2014 1st International Conference on Smart Computing, SMARTCOMP 2014, Hong Kong, 3-5 November 2014Visualization of high density streaming points has become a challenge in information exploration. In this paper, we present a new pipeline for the interactive visualization of large points set. The pipeline is based on the idea that heat-map can overcome the overlapping problem in visualization of high density streaming points. Thus, we firstly define a regular streaming format for large point set which can be updated or changed continually. Based on streaming points, we use kernel density estimation to estimate the point distribution and visualize the density image. Perceptive and interactive features are also considered in our visualization. To our knowledge, our pipeline is the first work that focuses on perceptive visualization of high density streaming points. The main step of our pipeline is accelerated via GPU rendering in order to make scene of real-time interaction in visualization. We demonstrate the visual effectiveness of our pipeline on a geographical dataset of high-density streaming points.Department of ComputingRefereed conference pape