| dc.description.abstract | According to previous researches, both driver and pedestrian distraction will have a serious impact on safety. Therefore, if the distracting behavior can be avoided, the probability of traffic accidents will be reduced. However, in the past, most researches focused on the detection of distracted driver. There are limited researches which focused to detect distraction among pedestrians. Therefore, the goal of this research is to improve both the driver and pedestrian safety by automatically detect distracted pedestrian. After accurately detecting the distracted pedestrian, interventions can be applied to improve traffic safety, such as sending warning messages or signals to drivers and pedestrians. In addition, this technology can also be used in self-driving car or advanced driver assistance system (ADAS). ADAS can help avoid accident through deceleration or other preventive methods after distracted pedestrian is detected.
In this paper, we propose a new distracted pedestrian detection method based on the OpenPose features, and propose a new CNN architecture, using OpenPose′s intermediate layer feature map as CNN input (OpenPose-based CNN), compared with using images as CNN input (image-based CNN), EER can be improved by 33.33% (EER = 8%). In addition, the experiment found that Skeleton-based SVM and OpenPose-based CNN can handle different type of data, so ensemble the two models can improve EER by 20% (EER = 6.4%). Finally, we try to use multiple continuously images for recognition, which can improve EER by 42.19% (EER = 3.7%) compared to use single image for recognition. | en_US |