SLIDE 1
Agenda
- Problem Definition
- Currently existing solutions
- Thesis statement
- Questions to answer
- Introduction about deep learning
Road Scene Understanding Presented by: Mohamed Mohsen Agenda - - PowerPoint PPT Presentation
Road Scene Understanding Presented by: Mohamed Mohsen Agenda - - PowerPoint PPT Presentation
Road Scene Understanding Presented by: Mohamed Mohsen Agenda Problem Definition Currently existing solutions Thesis statement Questions to answer Introduction about deep learning Problem Definition Detect roads with and
SLIDE 2
SLIDE 3
Problem Definition
- Detect roads with and without lane markings using
cameras
- Needed for autonomous driving
- Current solutions are extremely expensive
SLIDE 4
Currently used solutions
64 Laser Velodyne LIDAR System
SLIDE 5
Currently used solutions
64 Laser Velodyne LIDAR System
SLIDE 6
Currently used solutions
- Camera Based solutions have not reached satisfiable
accuracy
- Best accuracy achieved (using the KITTI dataset) is
91.76%
SLIDE 7
KITTI Dataset
Kitti Car
SLIDE 8
Thesis Statement
Improve the accuracy of road detection using cameras
SLIDE 9
Questions to answer
- What is the most suitable technique to use?
– Proposal: Deep learning
- What is the best model to use for feature representation?
- What is the best network architecture for this problem?
SLIDE 10
Introduction to Deep Learning
- Developed to address problems people found with other
techniques like backpropagation
- Addresses the problem of data representation
– Computers are better in finding the representations than
humans
- Emulates the way our brain works by having consecutive
layers each one computing a higher level function based
- n the output of the previous layer
SLIDE 11
Introduction to Deep Learning
Traditional Back Propagation
SLIDE 12
Introduction to Deep Learning
Representation Learning
SLIDE 13
Introduction to Deep Learning
Deep Learning Model
SLIDE 14
T h a n k Y
- u