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
Concept by Muhabit ul haq
Synopsis
As policymakers begin to develop standards for assessing and auditing algorithms, they must first define the class of decision-making or decision support tools to which their policies will apply. Leaving the term “algorithm” open to interpretation could place some of the models with the biggest impact beyond the reach of policies designed to ensure that such systems don’t hurt people.
By Kristian Lum and Rumman ChowdhuryDescribing a decision-making system as an “algorithm” is often a way to deflect accountability for human decisions. For many, the term implies a set of rules based objectively on empirical evidence or data. It also suggests a system that is highly complex perhaps so complex that a human would struggle to understand its inner workings or anticipate its behavior when deployed.But is this characterization
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