Pedestrian Friendly Traffic Signal Control.
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Pedestrian Friendly Traffic Signal Control.

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  • Abstract:
    This project continues research aimed at real-time detection and use of pedestrian

    traffic flow information to enhance adaptive traffic signal control in urban areas

    where pedestrian traffic is substantial and must be given appropriate attention and

    priority. Our recent work with Surtrac [12], a real-time adaptive signal control

    system for urban grid networks, has resulted in an extended intersection scheduling

    procedure that integrates sensed pedestrians and vehicles into aggregate multi-modal

    traffic flows and allocates green time on this integrated basis [17]. In this project we

    consider the companion problem of providing the pedestrian sensing capability

    necessary for effective use of this extended intersection scheduling procedure.

    Although some commercial pedestrian detection and counting capabilities do exist,

    they typically require the purchase and installation of additional higher resolution

    video camera technology, which can double the cost of detection per intersection.

    Our interest is in a solution that does not significantly increase infrastructure cost.

    The hypothesis investigated in this work is that lower resolution vehicle detection

    camera technology can be used to provide a relaxed form of pedestrian count data

    that is sufficient for incorporating pedestrian flow information into real-time

    intersection scheduling. Specifically, we study the possibility of extracting an

    approximate but usable measure of pedestrian “density” from the video stream of a

    commercial traffic camera. Our target functionality is the ability to qualitatively

    discriminate between “no”, “few” or “many” waiting pedestrians. Contemporary

    traffic camera technologies provide resolution as low as 320 × 240 gray scale images

    (see Figure 1), together with the ability to specify and monitor a set of occupancy

    zones within the image.

    Pedestrian detection and counting is not a hard task for humans, but it is challenging

    for computers. The challenges include diverse shapes and occlusion among

    pedestrians, a dynamic background, and low video quality. First, pedestrians can

    have various appearances because of clothing, accessories, assistive devices, and

    change of pose while walking. This high intra-class variation, as well as occlusion,

    makes pedestrian detection a hard problem. An alternative to classification based

    pedestrian detection is to find foreground pixels in each frame of the video, and

    analyze those pixels to estimate the number of pedestrians. However, since the

    system is deployed in an outdoor environment, shadows caused by moving objects or

    sudden change in illumination can create noise that complicates foreground

    detection. Moreover, to get a broader view of the intersection, the camera is installed

    at a certain height. Thus, pedestrians are small in the images and have fewer details for computer vision processing.

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