BigSnarf blog

Infosec FTW

Category Archives: Thoughts

3 Pillars of Autonomous Driving

T-SNE attack data

Self Driving Car Standards

Testing (Semi) Autonomous Cars With Tesla, Cadillac, Hyundai, and Mercedes

Tesla Autopilot is shown outperforming Mercedes, Hyundai and Cadillac in third-party tests

Tesla is about to increase its lead in semi-autonomous driving w/ ‘Tesla Vision’: computer vision based on NVIDIA’s parallel computing

 

definition programmer

cthl-m9wcaeub1y

GPS RTK

Obsessed with my Kaggle score – it’s just a number

screen-shot-2016-09-22-at-11-14-07-pm

“a state in which someone thinks about something constantly or frequently especially in a way that is not normal”

screen-shot-2016-09-26-at-1-06-27-pm

Three models grid search on 2 ubuntu boxes with a GTX1070 each

screen-shot-2016-10-01-at-7-57-02-am

First model I used was a ConvNet (CNN) with multiple Recurrent (RNN) layers on top.

Apparently, this is a common structure NN in speech. The CCN–>RNN tries to exploit temporal relationships within the data. In addition with the 2D structure of the spectrograms.

Imagine for each vertical line in the spectrogram as a timestep, the RNN is an attempt to  model the relationships between those timesteps.

The second model, I ripped out the RNN and replaced it with LSTM.  The third model, I tried to increase the RNN layers.

The fourth model combines all the electrodes in batches of 64. Fifth model is straight MLP in Tensorflow and Sixth model is straight CNN Tensorflow.

img_4689

screen-shot-2016-10-14-at-12-16-23-am

AWS p2.xlarge is a pain, just to try out a pretrained VGG model need 12gb GPU

screen-shot-2016-10-14-at-3-43-05-pm

Bad Data – Leaderboard hackingScreen Shot 2016-10-27 at 3.12.04 AM.png

img_4707

screen-shot-2016-10-29-at-6-01-12-pm

screen-shot-2016-10-29-at-11-49-26-am

screen-shot-2016-10-29-at-7-56-51-pm

Steering by Flashcards

screen-shot-2016-09-08-at-11-01-50-pm

screen-shot-2016-09-13-at-9-02-56-am

“To remove a bias towards driving straight the training data includes a higher proportion of frames that represent road curves”

 “to build a CNN to do lane following we only select data where the driver was staying in a lane and discard the rest. We then sample that video at 10 FPS.”

screen-shot-2016-09-13-at-10-02-37-am

CNN for:

  • Object-detection
  • Segmentation
  • Human pose estimation
  • Video classification
  • Object tracking
  • Superresolution

 

Image Links:

  1. https://arxiv.org/abs/1604.07316
  2. http://www.cv-foundation.org/openaccess/content_cvpr_2016_workshops/w3/papers/Gurghian_DeepLanes_End-To-End_Lane_CVPR_2016_paper.pdf
  3. https://www.ptgrey.com/case-study/id/10846
  4. http://net-scale.com/doc/net-scale-dave-report.pdf
  5. http://repository.cmu.edu/cgi/viewcontent.cgi?article=2874&context=compsci
  6. http://www.cv-foundation.org/openaccess/content_cvpr_2016_workshops/w3/papers/Gurghian_DeepLanes_End-To-End_Lane_CVPR_2016_paper.pdf
  7. https://drive.google.com/a/bench.co/file/d/0B9raQzOpizn1TkRIa241ZnBEcjQ/view
  8. https://culurciello.github.io/tech/2016/06/04/nets.html
  9. https://github.com/commaai/research/blob/master/SelfSteering.md
  10. https://research.googleblog.com/2016/08/improving-inception-and-image.html
  11. https://research.googleblog.com/2016/08/tf-slim-high-level-library-to-define.html
  12. https://github.com/tensorflow/models/blob/master/slim/deployment/model_deploy.py
  13. http://download.visinf.tu-darmstadt.de/data/from_games/
  14. https://github.com/tensorflow/models/tree/master/slim#fine-tuning-a-model-from-an-existing-checkpoint
  15. https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim
  16. https://github.com/tensorflow/models/blob/master/slim/README.md
  17. https://github.com/tensorflow/models/blob/master/slim/slim_walkthough.ipynb
  18. http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43022.pdf
  19. http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43442.pdf
  20. http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/44903.pdf
  21. http://arxiv.org/pdf/1409.1556.pdf
  22. https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf
  23. https://arxiv.org/abs/1512.03385

Resources:

  1. https://github.com/rwightman/tensorflow-litterbox
  2. https://github.com/tensorflow/models
  3. https://github.com/tensorflow/models/tree/master/slim
  4. https://github.com/facebook/fb.resnet.torch
  5. https://github.com/rbgirshick/py-faster-rcnn/
  6. https://github.com/KaimingHe/deep-residual-networks
  7. https://github.com/daijifeng001/mnc
  8. https://github.com/facebookresearch/multipathnet
  9. https://github.com/jcjohnson/neural-style
  10. https://www.quora.com/How-does-deep-residual-learning-work
  11. http://videolectures.net/deeplearning2016_montreal/
  12. http://academictorrents.com/details/743c16a18756557a67478a7570baf24a59f9cda6
  13. http://cs231n.github.io/
  14. http://www.deeplearningbook.org/
  15. http://cilvr.nyu.edu/doku.php?id=deeplearning:slides:start
  16. http://www0.cs.ucl.ac.uk/staff/d.silver/web/Teaching.html
  17. https://www.cs.ox.ac.uk/people/nando.defreitas/machinelearning/

 

 

Segmentation for training data

Vehicle Dynamics

Pedestrian Detection

Is it possible to perform pedestrian detection/classification using only LIDAR-based features?

http://cs229.stanford.edu/proj2015/172_report.pdf

http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/files/algorithms.pdf

https://github.com/titu1994/Inception-v4/blob/master/README.md

http://pjreddie.com/darknet/yolo/

http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLI-B1/563/2016/isprs-archives-XLI-B1-563-2016.pdf

http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/

http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43850.pdf

https://rodrigob.github.io/documents/2014_eccvw_ten_years_of_pedestrian_detection_with_supplementary_material.pdf

http://pascal.inrialpes.fr/data/human/

https://www.researchgate.net/figure/285407442_fig14_Figure-914-Pedestrian-detected-by-a-four-layer-Lidar-Pedestrian-detection-confidence

http://www6.in.tum.de/Main/Publications/Zhang2014b.pdf

https://www.ri.cmu.edu/pub_files/2009/7/navarro_et_al_fsr_09.pdf

http://onlinelibrary.wiley.com/doi/10.1002/rob.20312/abstract

http://home.isr.uc.pt/~cpremebida/files_cp/Exploiting%20LIDAR-based%20Features%20on%20Pedestrian%20Detection%20in%20Urban%20Scenarios.pdf

http://home.isr.uc.pt/~cpremebida/files_cp/LIDAR%20and%20vision-based%20pedestrian%20detection%20system.pdf

https://people.eecs.berkeley.edu/~carreira/papers/iros2014.pdf

https://github.com/bcal-lidar/tools/wiki/toolsusage

http://www.isprs-ann-photogramm-remote-sens-spatial-inf-sci.net/II-3-W4/103/2015/isprsannals-II-3-W4-103-2015.pdf

http://www.dimatura.net/extra/voxnet_maturana_scherer_iros15.pdf

http://www.vision.caltech.edu/Image_Datasets/CaltechPedestrians/

ROS

Lane Detect

http://www.vision.caltech.edu/malaa/research/lane-detection/

http://www.vision.caltech.edu/malaa/datasets/caltech-lanes/

http://www.vision.caltech.edu/malaa/software/research/caltech-lane-detection/

http://vclab.ca/wp-content/papercite-data/pdf/15-jei-j.pdf