| dc.description.abstract | Self-propelled vehicles with respect to the applications of artificially driving have many potential advantages, such as: increase drive space; short driving distance to avoid traffic accidents, driver fatigue caused by, among other factors, and automatically follow the trajectory of the leading self-propelled car navigation for transportation, cargo area has a considerable number of applications. In this study, by an autonomous control system, reduce the number of user′s burden; and self-propelled vehicle in addition to the system itself, only need a computer with an arduino and PTZ camera control panel, you can follow the complete trajectory of predecessor action.
Paper architecture is divided into three parts, detect and identify one predecessor, the second is the leader′s direction estimation and tracking, three are wheelchair control. Predecessor gradient direction is detected by the leading distribution (histograms of oriented gradients, HOG) as a feature, via support vector machines (support vector machine, SVM) classification determines whether the object is a pedestrian, and then has been determined for pedestrians the object is to take color maps do feature recognition whether the predecessor. The second part of the predecessor bearing estimation, is to obtain the relative coordinates predecessor and between self-propelled vehicle through the camera, plus a wheelchair position in the world coordinates to get the world′s leading coordinate. The third part of the implementation using a laptop equipped with Intel® Pentium® CoreTM i7-5700HQ 2.70GHz CPU and Wu′s technology electric wheelchairs Mambo 513 and Logitech′s QuickCam® Sphere AF PTZ color camera and Arduino uno control panel, through the computer calculates the distance traveled wheelchair delivered to Arduino control board with voltage mode output to effect control the wheelchair forward.
In the experimental analysis, we shot the film on campus streets and laboratory building lobby; pedestrian detection by capturing with vertical sides can reach 99% of the predecessor remove false positive rate of about 49%, with HOG features and SVM classifier detection rate about 85% false positive rate of about 0.1%. Wheelchair movement control can reach about 90% accuracy, the overall trajectory reproduce predecessor average error of less than 20 cm. System on the computer can perform operations per second processing speed of about 15 images. | en_US |