Convolutional Neural Networks with Neural Cascade Classifier for Pedestrian Detection

Tong, Bei; Fan, Bin; Wu, Fuchao

doi:10.1007/978-981-10-3002-4_21

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“…Although they had achieved good performance, sophisticated operations limit their practical use and may be time-consuming as well. Bei Tong et al present an end-to-end network based on faster R-CNN and neural cascade classifier for pedestrian detection in [25]. Different from faster R-CNN which only makes use of the last convolutional layer, they utilize features from multiple layers and feed them to a neural cascade classifier.…”

Section: Deep Neural Networkmentioning

confidence: 99%

Survey on Artificial Intelligence for Vehicles

Cheng

Guo

et al. 2018

Automot. Innov.

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With the ever-increasing demand in urban mobility and modern logistics sector, the vehicle population has been steadily growing over the past several decades. One natural consequence of the vehicle population growth is the increase in traffic congestion. Almost all (metropolitan) cities including the major ones, like Los Angeles, Beijing, New York, are suffering from heavy traffic congestion. Statistics show that, in 2015, 43 cities in China are suffering a prolonged travel time of more than 1.5 h every day during rush hours. In the meanwhile, traffic accidents are plaguing the economic development as well.

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Section: Deep Neural Networkmentioning

confidence: 99%