In this paper, the authors focused on the mechanism that using untrained crowdworkers to find and label accessibility problems in Google Street View imagery. They provide the workers images from Google Street View imagery to let them find, label and access sidewalk accessibility problems. They compared the results of this labeling task completed by six dedicated labelers including three wheelchair users and by MTurk workers. The comparison shows that the crowdworkers can determining the presence of an accessibility problem with high accuracy, which means this mechanism is promising about sidewalk accessibility. However, that mechanism still have problems such as locating the GSV camera in geographic space and selecting an optimal viewpoint, sidewalk width problem and the age of the images. In the experiments, the workers cannot label some of the images due to camera position, and the images may be captured three years ago. Additionally, there is no method to measure the width of the sidewalk, which is a need by the wheelchair users.
Reflections:
The authors combined the Google Street View imagery and MTurk Crowdsourcing to build a system which can detect accessibility challenges. This kind of hybrid system has a high accuracy in the finding and labeling of such kind of accessibility challenges. If this system can be used practically, the disables will benefit a lot with the help of the system.
However, there is some problems in the system. As is mentioned in the paper, the images in the Google Street View are old. Some of the images may be captured years ago. If the detection is based on these pictures, some new access problems will be detected. For this problem, I have a rough idea about letting the users of the system to update the image library. When they found some difference between the images from library and practical sidewalk, they can upload the latest pictures captured by them. As a result, other users will not suffer from the images’ age problem. However, this solution will change the whole system. Google Street View imagery requires professional capture devices which is not available to most of the users. As a result, the Google Street View will not update its imagery using the photos captured by the users, and the system cannot update itself using the imagery. Instead, the system has to build its own image library, which is totally different from the introduced system in the paper. Additionally, the photos provided by the users may be with low resolution, and it will be difficult for the MTurk workers to label the accessibility challenges.
Similarly, the problem that the workers cannot measure the width of the sidewalk can be solved if users can upload the width when they are using the system. However, it still faces the problem of lacking an own database and the system needs to be modified hugely.
Instead of detecting accessibility challenges, I think the system is more useful in tracking and labeling bike lanes. Compared with the accessibility of sidewalk, to detect the existence of bike lanes will suffer less from the age problem, because even the bike lanes were built years ago, they can still work. Also, there is no need to measure the width of the lanes, as all the lanes should have enough space for bikes to pass.
Question:
Is there any approach to solve the age problem, camera point problem and measuring width problem in the system?
What do you think about applying such a system to track and label bike lanes?
What other kinds of street detection problems can this system being applied to?
