Acerca de este Curso
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Fechas límite flexibles

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Nivel avanzado

This is an advanced course, intended for learners with a background in computer vision and deep learning.

Aprox. 20 horas para completar

Sugerido: 6 weeks of study, 5-6 hours per week...

Inglés (English)

Subtítulos: Inglés (English)

Qué aprenderás

  • Check

    Work with the pinhole camera model, and perform intrinsic and extrinsic camera calibration

  • Check

    Detect, describe and match image features and design your own convolutional neural networks

  • Check

    Apply these methods to visual odometry, object detection and tracking

  • Check

    Apply semantic segmentation for drivable surface estimation

Los estudiantes que toman este Course son
  • Machine Learning Engineers
  • Data Scientists
  • Researchers
  • Engineers
  • Scientists

100 % en línea

Comienza de inmediato y aprende a tu propio ritmo.

Fechas límite flexibles

Restablece las fechas límite en función de tus horarios.

Nivel avanzado

This is an advanced course, intended for learners with a background in computer vision and deep learning.

Aprox. 20 horas para completar

Sugerido: 6 weeks of study, 5-6 hours per week...

Inglés (English)

Subtítulos: Inglés (English)

Aspectos destacados del curso

featured

Enseñanza de vanguardia

Accede a investigaciones innovadoras

Steven Waslander te demuestra la importancia de la detección de objetos en un entorno 3D completo con automóviles, peatones, árboles y condiciones climáticas.

Programa - Qué aprenderás en este curso

Semana
1
2 horas para completar

Welcome to Course 3: Visual Perception for Self-Driving Cars

4 videos (Total 18 minutos), 4 lecturas
4 videos
Welcome to the course4m
Meet the Instructor, Steven Waslander5m
Meet the Instructor, Jonathan Kelly2m
4 lecturas
Course Prerequisites15m
How to Use Discussion Forums15m
How to Use Supplementary Readings in This Course15m
Recommended Textbooks15m
7 horas para completar

Module 1: Basics of 3D Computer Vision

6 videos (Total 43 minutos), 4 lecturas, 2 cuestionarios
6 videos
Lesson 1 Part 2: Camera Projective Geometry8m
Lesson 2: Camera Calibration7m
Lesson 3 Part 1: Visual Depth Perception - Stereopsis7m
Lesson 3 Part 2: Visual Depth Perception - Computing the Disparity5m
Lesson 4: Image Filtering7m
4 lecturas
Supplementary Reading: The Camera Sensor30m
Supplementary Reading: Camera Calibration15m
Supplementary Reading: Visual Depth Perception30m
Supplementary Reading: Image Filtering15m
1 ejercicio de práctica
Module 1 Graded Quiz30m
Semana
2
7 horas para completar

Module 2: Visual Features - Detection, Description and Matching

6 videos (Total 44 minutos), 5 lecturas, 1 cuestionario
6 videos
Lesson 2: Feature Descriptors6m
Lesson 3 Part 1: Feature Matching7m
Lesson 3 Part 2: Feature Matching: Handling Ambiguity in Matching5m
Lesson 4: Outlier Rejection8m
Lesson 5: Visual Odometry9m
5 lecturas
Supplementary Reading: Feature Detectors and Descriptors30m
Supplementary Reading: Feature Matching15m
Supplementary Reading: Feature Matching15m
Supplementary Reading: Outlier Rejection15m
Supplementary Reading: Visual Odometry10m
Semana
3
3 horas para completar

Module 3: Feedforward Neural Networks

6 videos (Total 58 minutos), 6 lecturas, 1 cuestionario
6 videos
Lesson 2: Output Layers and Loss Functions10m
Lesson 3: Neural Network Training with Gradient Descent10m
Lesson 4: Data Splits and Neural Network Performance Evaluation8m
Lesson 5: Neural Network Regularization9m
Lesson 6: Convolutional Neural Networks9m
6 lecturas
Supplementary Reading: Feed-Forward Neural Networks15m
Supplementary Reading: Output Layers and Loss Functions15m
Supplementary Reading: Neural Network Training with Gradient Descent15m
Supplementary Reading: Data Splits and Neural Network Performance Evaluation10m
Supplementary Reading: Neural Network Regularization15m
Supplementary Reading: Convolutional Neural Networks10m
1 ejercicio de práctica
Feed-Forward Neural Networks30m
Semana
4
3 horas para completar

Module 4: 2D Object Detection

4 videos (Total 52 minutos), 4 lecturas, 1 cuestionario
4 videos
Lesson 2: 2D Object detection with Convolutional Neural Networks11m
Lesson 3: Training vs. Inference11m
Lesson 4: Using 2D Object Detectors for Self-Driving Cars14m
4 lecturas
Supplementary Reading: The Object Detection Problem15m
Supplementary Reading: 2D Object detection with Convolutional Neural Networks30m
Supplementary Reading: Training vs. Inference45m
Supplementary Reading: Using 2D Object Detectors for Self-Driving Cars30m
1 ejercicio de práctica
Object Detection For Self-Driving Cars30m
4.6
14 revisionesChevron Right

Principales revisiones sobre Visual Perception for Self-Driving Cars

por RGOct 7th 2019

Many thanks for this amazing course!!!! was very hard to me but I have learned a lot!!! Thanks!!!

por AAJul 18th 2019

Content is great but lack of instructor support makes the course hard to understand.

Instructor

Avatar

Steven Waslander

Associate Professor
Aerospace Studies

Acerca de Universidad de Toronto

Established in 1827, the University of Toronto is one of the world’s leading universities, renowned for its excellence in teaching, research, innovation and entrepreneurship, as well as its impact on economic prosperity and social well-being around the globe. ...

Acerca de Programa especializado Automóviles de auto conducción

Be at the forefront of the autonomous driving industry. With market researchers predicting a $42-billion market and more than 20 million self-driving cars on the road by 2025, the next big job boom is right around the corner. This Specialization gives you a comprehensive understanding of state-of-the-art engineering practices used in the self-driving car industry. You'll get to interact with real data sets from an autonomous vehicle (AV)―all through hands-on projects using the open source simulator CARLA. Throughout your courses, you’ll hear from industry experts who work at companies like Oxbotica and Zoox as they share insights about autonomous technology and how that is powering job growth within the field. You’ll learn from a highly realistic driving environment that features 3D pedestrian modelling and environmental conditions. When you complete the Specialization successfully, you’ll be able to build your own self-driving software stack and be ready to apply for jobs in the autonomous vehicle industry. It is recommended that you have some background in linear algebra, probability, statistics, calculus, physics, control theory, and Python programming. You will need these specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers)....
Automóviles de auto conducción

Preguntas Frecuentes

  • Una vez que te inscribes para obtener un Certificado, tendrás acceso a todos los videos, cuestionarios y tareas de programación (si corresponde). Las tareas calificadas por compañeros solo pueden enviarse y revisarse una vez que haya comenzado tu sesión. Si eliges explorar el curso sin comprarlo, es posible que no puedas acceder a determinadas tareas.

  • Cuando te inscribes en un curso, obtienes acceso a todos los cursos que forman parte del Programa especializado y te darán un Certificado cuando completes el trabajo. Se añadirá tu Certificado electrónico a la página Logros. Desde allí, puedes imprimir tu Certificado o añadirlo a tu perfil de LinkedIn. Si solo quieres leer y visualizar el contenido del curso, puedes auditar el curso sin costo.

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