Abstract
Pedestrian motion trajectory prediction is an important task in intelligent driving, and it can provide a valuable reference for the subsequent path decision of intelligent driving. However, so far, there are only a few models in the field of specific pedestrian motion track prediction in intelligent driving from far shot first-person perspective video. To accomplish this task, we proposed a deep learning model for pedestrian motion trajectory prediction from far shot first-person perspective video with four key innovations: a) A macroscopic pedestrian trajectory prediction module is established under the close correlation between neighboring frames to estimate the pedestrian motion track on the whole; b) A relative motion transformation module of vehicle-mounted camera is designed to consider the effect of vehicle-mounted camera s ego-motion on the pedestrian motion track; c) We set up a circular training module to maintain the number of parameters in our model to simplify
Pedram, you saw vehicles floating down that makeshift river taking with it all sorts of objects. what s happening there? it is such a dangerous scenario that we see this in the united states. flooding in the united states is number one weather-related killer. often times half of the time when it comes to fatalities they occur inside of vehicles. it s something worth noting. i want to show you the storm that caused the mess. according to some shipping vessels, it produced gusts of 110 miles an hour. significant waves as well. bringing down 95 to 100 millimeters. four inches of rain in 24 hours over the region. i want to bring the maps in to motion. animation of what little water it takes to cause significant flooding. 150 millimeters, or six inches of moving water. that can knock an adult off their feet. bring that up to say 600 millimeters or two feet and that will move a vehicle downstream.