Visual Perception for Self-Driving Cars

Go to Course: https://www.coursera.org/learn/visual-perception-self-driving-cars

Introduction

### Course Review: Visual Perception for Self-Driving Cars **Overview** If you're intrigued by the future of transportation and want to dive deep into the technology that powers autonomous vehicles, look no further than "Visual Perception for Self-Driving Cars." Offered by the University of Toronto as part of their Self-Driving Cars Specialization, this course is tailored for learners who wish to grasp the complex landscape of visual perception critical for autonomous driving. It covers essential topics such as object detection, computer vision methods, and the mathematical foundations required for robotic perception. **Course Content and Structure** The course is well-structured over six engaging modules, each contributing to a comprehensive understanding of visual perception: 1. **Basics of 3D Computer Vision**: Here, learners are introduced to camera models, calibration techniques (intrinsic and extrinsic), and the principles of monocular and stereo vision. This foundational knowledge sets the stage for more advanced topics. 2. **Visual Features - Detection, Description, and Matching**: This module delves into tracking motion and recognizing locations using visual features. The importance of feature extraction for object detection and semantic segmentation is also highlighted, laying the groundwork for practical applications in real-time perception. 3. **Feedforward Neural Networks**: Understanding deep learning is invaluable for modern perception tasks. This module explains core concepts behind convolutional neural networks (CNNs), focusing on architectures and components critical for successful object detection and segmentation tasks. 4. **2D Object Detection**: Building on previous knowledge, this section covers techniques for detecting various objects like pedestrians, cyclists, and vehicles. It presents foundational methods for building a robust self-driving perception pipeline. 5. **Semantic Segmentation**: This module emphasizes associating image pixels with specific labels to identify objects within the driving environment. Learning how segmentation integrates with object detection provides a well-rounded view of ensuring safe navigation. 6. **Putting it Together - Perception of Dynamic Objects in the Drivable Region**: The final project allows students to implement a collision warning system to recognize dynamic obstacles in a vehicle's path. This hands-on application solidifies the learner's grasp of concepts through practical implementation. **Why You Should Enroll** 1. **Expert Instruction**: The course is delivered by the University of Toronto, ensuring a high standard of education. The instructors present complex topics in an understandable manner, making it accessible for those eager to learn. 2. **Hands-On Projects**: The culmination of the course in practical projects encourages active learning and application of theoretical concepts. This real-world approach to learning helps solidify your understanding and prepares you for professional environments. 3. **In-Demand Skills**: As self-driving technology continues to advance, skills in visual perception and computer vision are increasingly valuable. Completing this course could enhance your employability in fields related to Autonomous Systems, Robotics, and Artificial Intelligence. 4. **Community and Resources**: Coursera provides a platform for discussion and collaboration among peers, fostering a community of learners who share similar interests. Supplemental resources and readings further enhance the learning experience. **Conclusion** "Visual Perception for Self-Driving Cars" is a must-take course for anyone interested in the mechanics of autonomous vehicles and the future of transportation technology. Whether you're looking to enhance your existing skill set or venture into a new career path, this course offers the perfect balance of theory and application. With its well-rounded syllabus, esteemed instruction, and hands-on projects, I highly recommend it to aspiring engineers and computer scientists alike. Don't miss the chance to be part of the future – enroll today and start your journey into the fascinating world of self-driving cars!

Syllabus

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

This module introduces the main concepts from the broad and exciting field of computer vision needed to progress through perception methods for self-driving vehicles. The main components include camera models and their calibration, monocular and stereo vision, projective geometry, and convolution operations.

This module introduces the main concepts from the broad field of computer vision needed to progress through perception methods for self-driving vehicles. The main components include camera models and their calibration, monocular and stereo vision, projective geometry, and convolution operations.

Visual features are used to track motion through an environment and to recognize places in a map. This module describes how features can be detected and tracked through a sequence of images and fused with other sources for localization as described in Course 2. Feature extraction is also fundamental to object detection and semantic segmentation in deep networks, and this module introduces some of the feature detection methods employed in that context as well.

Deep learning is a core enabling technology for self-driving perception. This module briefly introduces the core concepts employed in modern convolutional neural networks, with an emphasis on methods that have been proven to be effective for tasks such as object detection and semantic segmentation. Basic network architectures, common components and helpful tools for constructing and training networks are described.

The two most prevalent applications of deep neural networks to self-driving are object detection, including pedestrian, cyclists and vehicles, and semantic segmentation, which associates image pixels with useful labels such as sign, light, curb, road, vehicle etc. This module presents baseline techniques for object detection and the following module introduce semantic segmentation, both of which can be used to create a complete self-driving car perception pipeline.

The second most prevalent application of deep neural networks to self-driving is semantic segmentation, which associates image pixels with useful labels such as sign, light, curb, road, vehicle etc. The main use for segmentation is to identify the drivable surface, which aids in ground plane estimation, object detection and lane boundary assessment. Segmentation labels are also being directly integrated into object detection as pixel masks, for static objects such as signs, lights and lanes, and moving objects such cars, trucks, bicycles and pedestrians.

The final module of this course focuses on the implementation of a collision warning system that alerts a self-driving car about the position and category of obstacles present in their lane. The project is comprised of three major segments: 1) Estimating the drivable space in 3D, 2) Semantic Lane Estimation and 3) Filter wrong output from object detection using semantic segmentation.