Computer Vision and Perception for Self-Driving Cars (Deep Learning Course)

computer_vision_perception_self_driving_cars.txt

Computer Vision and Perception for Self-Driving Cars (Deep Learning Course)
https://www.youtube.com/watch?v=cPOtULagNnI

Python + Deep Learning

Robotics with Sakshay
https://www.youtube.com/c/roboticswithsakshay/videos

* Road Segmentation
* 2D Object Detection (yolo)
* Object tracking (deep sought)
* 3D data visualisation
* Multi task learning (depth estimation & semantic segmentation)
* 3D object detection
* Bird's eye view (transformers)

⌨️ (0:02:16) Fully Convolutional Network | Road Segmentation
   🔗 Kaggle Dataset: https://www.kaggle.com/sakshaymahna/kittiroadsegmentation
   🔗 Kaggle Notebook: https://www.kaggle.com/sakshaymahna/fully-convolutional-network
   🔗 KITTI Dataset: http://www.cvlibs.net/datasets/kitti/
   🔗 Fully Convolutional Network Paper: https://arxiv.org/abs/1411.4038
   🔗 Hand Crafted Road Segmentation: https://www.youtube.com/watch?v=hrin-qTn4L4 (Udacity Self Driving Cars Advanced Lane Detection)
   🔗 Deep Learning and CNNs: https://www.youtube.com/watch?v=aircAruvnKk (But what is a neural network? | Chapter 1, Deep learning)
   
   Transposed convolutions - better than upscale interpolations!
   VGG16 - encoder, decoder
   
   Replace Add with Concatenate
   Replace Concatenate with Conv2DTranspose (seems not that great!)
   
⌨️ (0:20:45) YOLO | 2D Object Detection
   🔗 Kaggle Competition/Dataset: https://www.kaggle.com/c/3d-object-detection-for-autonomous-vehicles
   🔗 Visualization Notebook: https://www.kaggle.com/sakshaymahna/lyft-3d-object-detection-eda
   🔗 YOLO Notebook: https://www.kaggle.com/sakshaymahna/yolov3-keras-2d-object-detection
   🔗 Playlist on Fundamentals of Object Detection: https://www.youtube.com/playlist?list=PL_IHmaMAvkVxdDOBRg2CbcJBq9SY7ZUvs
   🔗 Blog on YOLO: https://www.section.io/engineering-education/introduction-to-yolo-algorithm-for-object-detection/
   🔗 YOLO Paper: https://arxiv.org/abs/1506.02640
   
⌨️ (0:35:51) Deep SORT | Object Tracking
   🔗 Dataset: https://www.kaggle.com/sakshaymahna/kittiroadsegmentation
   🔗 Notebook/Code: https://www.kaggle.com/sakshaymahna/deepsort/notebook
   🔗 Blog on Deep SORT: https://medium.com/analytics-vidhya/object-tracking-using-deepsort-in-tensorflow-2-ec013a2eeb4f
   🔗 Deep SORT Paper: https://arxiv.org/abs/1703.07402
   🔗 Kalman Filter: https://www.youtube.com/playlist?list=PLn8PRpmsu08pzi6EMiYnR-076Mh-q3tWr
   🔗 Hungarian Algorithm: https://www.geeksforgeeks.org/hungarian-algorithm-assignment-problem-set-1-introduction/
   🔗 Cosine Distance Metric: https://www.machinelearningplus.com/nlp/cosine-similarity/
   🔗 Mahalanobis Distance: https://www.machinelearningplus.com/statistics/mahalanobis-distance/
   🔗 YOLO Algorithm: https://youtu.be/C3qmhPVUXiE
   
 Simple Online Realtime Tracking
 Bounding box prediction, Kalman Filters, Linear Approximation, IoU matching techniques
 Deal with occlusion; Linear velocity model
 Mahalanobis distance -> similarity between point and probability distribution
 Longer occlusion periods present a problem
 
 ⌨️ (0:52:37) KITTI 3D Data Visualization | Homogenous Transformations
   🔗 Dataset: https://www.kaggle.com/garymk/kitti-3d-object-detection-dataset
   🔗 Notebook/Code: https://www.kaggle.com/sakshaymahna/lidar-data-visualization/notebook
   🔗 LIDAR: https://geoslam.com/what-is-lidar/
   🔗 Tesla doesn't use LIDAR: https://towardsdatascience.com/why-tesla-wont-use-lidar-57c325ae2ed5
   
Homogeneous transformations; point clouds;

⌨️ (1:06:45) Multi Task Attention Network (MTAN) | Multi Task Learning
   🔗 Dataset: https://www.kaggle.com/sakshaymahna/cityscapes-depth-and-segmentation
   🔗 Notebook/Code: https://www.kaggle.com/sakshaymahna/mtan-multi-task-attention-network
   🔗 Data Visualization: https://www.kaggle.com/sakshaymahna/exploratory-data-analysis
   🔗 MTAN Paper: https://arxiv.org/abs/1803.10704
   🔗 Blog on Multi Task Learning: https://ruder.io/multi-task/
   🔗 Image Segmentation and FCN: https://youtu.be/U_v0Tovp4XQ
   
Encoder / Decoder / Attention submodules

⌨️ (1:20:58) SFA 3D | 3D Object Detection
   🔗 Dataset: https://www.kaggle.com/garymk/kitti-3d-object-detection-dataset
   🔗 Notebook/Code: https://www.kaggle.com/sakshaymahna/sfa3d
   🔗 Data Visualization: https://www.kaggle.com/sakshaymahna/l...
   🔗 Data Visualization Video: https://www.youtube.com/watch?v=tb1H42kE0eE
   🔗 SFA3D GitHub Repository: https://github.com/maudzung/SFA3D
   🔗 Feature Pyramid Networks: https://jonathan-hui.medium.com/understanding-feature-pyramid-networks-for-object-detection-fpn-45b227b9106c
   🔗 Keypoint Feature Pyramid Network: https://arxiv.org/pdf/2001.03343.pdf
   🔗 Heat Maps: https://en.wikipedia.org/wiki/Heat_map
   🔗 Focal Loss: https://medium.com/visionwizard/understanding-focal-loss-a-quick-read-b914422913e7
   🔗 L1 Loss: https://afteracademy.com/blog/what-are-l1-and-l2-loss-functions
   🔗 Balanced L1 Loss: https://paperswithcode.com/method/balanced-l1-loss
   🔗 Learning Rate Decay: https://medium.com/analytics-vidhya/learning-rate-decay-and-methods-in-deep-learning-2cee564f910b
   🔗 Cosine Annealing: https://paperswithcode.com/method/cosine-annealing
   
 Super Fast and Accurate 3D Object detection
 Feature Pyramid network
 
 ⌨️ (1:40:24) UNetXST | Camera to Bird's Eye View
   🔗 Dataset: https://www.kaggle.com/sakshaymahna/semantic-segmentation-bev
   🔗 Dataset Visualization: https://www.kaggle.com/sakshaymahna/data-visualization
   🔗 Notebook/Code: https://www.kaggle.com/sakshaymahna/unetxst
   🔗 UNetXST Paper: https://arxiv.org/pdf/2005.04078.pdf
   🔗 UNetXST Github Repository: https://github.com/ika-rwth-aachen/Cam2BEV
   🔗 UNet: https://towardsdatascience.com/understanding-semantic-segmentation-with-unet-6be4f42d4b47
   🔗 Image Transformations: https://kevinzakka.github.io/2017/01/10/stn-part1/
   🔗 Spatial Transformer Networks: https://kevinzakka.github.io/2017/01/18/stn-part2/