Pedestrian detection network with multi-modal cross-guided learning

2022 ◽  
pp. 103370
Author(s):  
ChunJian Hua ◽  
MingChun Sun ◽  
Yu Zhu ◽  
Yi Jiang ◽  
JianFeng Yu ◽  
...  
Keyword(s):  
Pedestrian Detection ◽  
Guided Learning

Review of Giving Emphasis to Guided Learning.

1967 ◽  
Vol 12 (4) ◽  
pp. 236-236
Author(s):  
WAYNE H. HOLTZMAN
Keyword(s):  
Guided Learning

Attitudes toward the use of TELL tools in English language learning among Vietnamese Tertiary English majors

2019 ◽  
Vol 5 (5) ◽  
pp. 581-596
Keyword(s):  
Language Learning ◽  
Language Proficiency ◽  
English Language ◽  
English Language Learning ◽  
Guided Learning ◽  
Positive Attitudes ◽  
Significant Difference ◽  
English Majors ◽  
Very High ◽  
Different Levels

Technology plays a crucial role in the self-guided learning of a second language in general and English in particular. Nevertheless, many students in different contexts still ignore the application of technology-enhanced language learning (TELL) tools in enhancing their foreign language proficiency. Therefore, this study is conducted to investigate the attitudes towards the use of TELL tools in English-language learning (ELL) among English majors at one university in Vietnam. To collect data, 197 English majors participated in finishing the questionnaire, and 20 students were invited to join the interviews. The findings are that the majority of students have positive attitudes towards the use of TELL tools and the frequency of using these tools is very high. In addition, the results also reveal that there is no significant difference in attitudes towards and frequency of using TELL tools in learning English in terms of the year of study. However, students of different levels of academic achievements have different attitudes towards using TELL tools and use TELL tools to learn English differently. Received 2nd May 2019; Revised 16th July 2019, Accepted 20th October 2019

Deep Learning for Pedestrian Detection

2019 ◽  
Vol 9 (8) ◽  
pp. p9212
Author(s):  
Utkarsha Sagar ◽  
Ravi Raja ◽  
Himanshu Shekhar
Keyword(s):  
Deep Learning ◽  
Pedestrian Detection

Automotive Radars

2017 ◽  
Author(s):  
Sujeet Patole ◽  
Murat Torlak ◽  
Dan Wang ◽  
Murtaza Ali
Keyword(s):  
Signal Processing ◽  
Pedestrian Detection ◽  
Radar Signal ◽  
Automotive Radar ◽  
Estimation Algorithms ◽  
Radar Systems ◽  
Starting Point ◽  
Processing Techniques ◽  
Automotive Radars ◽  
Signal Processing Techniques

Automotive radars, along with other sensors such as lidar, (which stands for “light detection and ranging”), ultrasound, and cameras, form the backbone of self-driving cars and advanced driver assistant systems (ADASs). These technological advancements are enabled by extremely complex systems with a long signal processing path from radars/sensors to the controller. Automotive radar systems are responsible for the detection of objects and obstacles, their position, and speed relative to the vehicle. The development of signal processing techniques along with progress in the millimeter- wave (mm-wave) semiconductor technology plays a key role in automotive radar systems. Various signal processing techniques have been developed to provide better resolution and estimation performance in all measurement dimensions: range, azimuth-elevation angles, and velocity of the targets surrounding the vehicles. This article summarizes various aspects of automotive radar signal processing techniques, including waveform design, possible radar architectures, estimation algorithms, implementation complexity-resolution trade-off, and adaptive processing for complex environments, as well as unique problems associated with automotive radars such as pedestrian detection. We believe that this review article will combine the several contributions scattered in the literature to serve as a primary starting point to new researchers and to give a bird’s-eye view to the existing research community.

Perception

2018 ◽  
Author(s):  
Joel Z. Leibo ◽  
Tomaso Poggio
Keyword(s):  
Computational Neuroscience ◽  
Feature Detection ◽  
Pedestrian Detection ◽  
Detection Systems ◽  
Catching Up ◽  
Feature Detectors ◽  
Recognition Systems ◽  
Engineered Systems ◽  
Perceptual Systems ◽  
The Brain

This chapter provides an overview of biological perceptual systems and their underlying computational principles focusing on the sensory sheets of the retina and cochlea and exploring how complex feature detection emerges by combining simple feature detectors in a hierarchical fashion. We also explore how the microcircuits of the neocortex implement such schemes pointing out similarities to progress in the field of machine vision driven deep learning algorithms. We see signs that engineered systems are catching up with the brain. For example, vision-based pedestrian detection systems are now accurate enough to be installed as safety devices in (for now) human-driven vehicles and the speech recognition systems embedded in smartphones have become increasingly impressive. While not being entirely biologically based, we note that computational neuroscience, as described in this chapter, makes up a considerable portion of such systems’ intellectual pedigree.

Domains of Socialization

2019 ◽  
pp. 72-90
Author(s):  
Joan E. Grusec
Keyword(s):  
Young Adults ◽  
Zone Of Proximal Development ◽  
Proximal Development ◽  
Group Participation ◽  
Guided Learning ◽  
Positive Action ◽  
Affect And Cognition ◽  
And Control ◽  
Value Learning ◽  
Over Time

This chapter surveys how behavior, affect, and cognition with respect to parenting and moral development have been conceptualized over time. It moves to a discussion of domains of socialization; that is, different contexts in which socialization occurs and where different mechanisms operate. Domains include protection where the child is experiencing negative affect, reciprocity where there is an exchange of favors, group participation or learning through observing others and engaging with them in positive action, guided learning where values are taught in the child’s zone of proximal development, and control where values are learned through discipline and reward. Research using narratives of young adults about value-learning events suggests that inhibition of antisocial behavior is more likely learned in the control domain, and prosocial behavior more likely in the group participation domain. Internalization of values, measured by narrative meaningfulness, is most likely in the group participation domain.

Three-stage RGBD architecture for vehicle and pedestrian detection using convolutional neural networks and stereo vision

2020 ◽  
Vol 14 (10) ◽  
pp. 1319-1327 ◽  
Author(s):  
Pedro Augusto Pinho Ferraz ◽  
Bernardo Augusto Godinho de Oliveira ◽  
Flávia Magalhães Freitas Ferreira ◽  
Carlos Augusto Paiva da Silva Martins
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Stereo Vision ◽  
Pedestrian Detection
Sign in / Sign up

Export Citation Format

Share Document