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Learn about Computer Vision and Perception for Self Driving Cars. This series focuses on the different tasks that a Self Driving Car Perception unit would be required to do. ✏️ Course by Robotics with Sakshay. https://www.youtube.com/channel/UC57lEMTXZzXYu_y0FKdW6xA Try courses in your browser: https://scrimba.com/freeCodeCamp-Python (Made possible by a grant from Scrimba) Contents ⌨️ (0:00:00) Introduction ⌨️ (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 🔗 Deep Learning and CNNs: https://www.youtube.com/watch?v=aircAruvnKk ⌨️ (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 ⌨️ (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 ⌨️ (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 ⌨️ (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://youtu.be/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 ⌨️ (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/

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