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Monthly Archives: February 2017

Speed control for safety

Note: bouncy image needs vectors stabilized by estimating image-based ego-motion estimation. Then you can measure the speed of objects in the driving window. Horizontal based ego-motions are probably lane dumpers. This estimation could predict lane dumpers and should affect speed and enhanced by other sensors.

http://www.6d-vision.com/home/prinzip

http://www.6d-vision.com/9-literatur

http://www.schneidertools.com/wp-content/uploads/2017/01/icpr16-johnny-9.pdf

http://www.frc.ri.cmu.edu/~jizhang03/Publications/ICRA_2015.pdf

http://cs.stanford.edu/group/manips/publications/pdfs/Petrovskaya_2009_ICRA.pdf

https://balzer82.github.io/Kalman/

  1. What is the distance to each of the objects
  2. What is the speed of the current trajectory?
  3. Optical flow for moving objects
  4. Remove bouncy image with prediction
  5. Filter stationary objects
  6. What objects are left to predict from?
  7. Calculate speed and directions of moving objects
  8. Which objects are travelling with me?
  9. What objects are travelling into my path?

https://github.com/DLuensch/StereoVision-ADCensus

https://arxiv.org/abs/1609.04653

https://github.com/bigsnarfdude/StereoVision-ADCensus

http://www.6d-vision.com/aktuelle-forschung/ego-motion-estimation

https://github.com/bigsnarfdude/StereoVision-ADCensus

http://www.robesafe.uah.es/index.php?option=com_jresearch&view=publication&task=show&id=22&Itemid=66

http://www.6d-vision.com/9-literatur/keller_dagm11

https://arxiv.org/pdf/1409.7963.pdf

http://cs231n.stanford.edu/reports2016/112_Report.pdf

http://3dvis.ri.cmu.edu/data-sets/localization/

https://arxiv.org/pdf/1702.07600.pdf

https://github.com/erget/stereovision

https://github.com/JuanTarrio/rebvo

http://asrl.utias.utoronto.ca/~tdb/bib/dong_masc13.pdf

http://www.edwardrosten.com/work/videos/index.html

https://github.com/bigsnarfdude/pykitti

FlowNet: Learning Optical Flow with Convolutional Networks https://arxiv.org/abs/1504.06852

http://www.cvlibs.net/projects.php

http://3dvis.ri.cmu.edu/data-sets/localization/

http://www.robots.ox.ac.uk/NewCollegeData/

http://robots.engin.umich.edu/SoftwareData/Ford

https://www.coursera.org/learn/robotics-perception/lecture/ReEv0/visual-odometry

http://www.cs.toronto.edu/~urtasun/courses/CSC2541/03_odometry.pdf

http://www.6d-vision.com/aktuelle-forschung/ego-motion-estimation

http://www.cvlibs.net/datasets/kitti/eval_odometry.php

https://avisingh599.github.io/vision/visual-odometry-full/

https://arxiv.org/pdf/1609.04653.pdf

https://en.wikipedia.org/wiki/Visual_odometry

https://sourceforge.net/projects/qcv/

http://docs.opencv.org/3.2.0/d7/d8b/tutorial_py_lucas_kanade.html

http://wiki.ros.org/viso2_ros

https://github.com/uzh-rpg/rpg_svo

http://www.hessmer.org/blog/2010/08/17/monocular-visual-odometry/

https://github.com/hovren/crisp/tree/master

http://www.eng.auburn.edu/~troppel/courses/00sum13/7970%202013A%20ADvMobRob%20sp13/literature/vis%20odom%20tutor%20part1%20.pdf

Optical Flow

 

 

A subject is to provide a pedestrian motion predicting device capable of accurately predicting a possibility of a rush out before a pedestrian actually begins to rush out. According to the embodiments, the pedestrian is detected from input image data, a portion in which the detected pedestrian is imaged is cut out from the image data, a shape of the pedestrian imaged in the cut-out partial image data is classified by collating the shape with a learning-finished identifier group or a pedestrian recognition template group, and the rush out of the pedestrian is predicted based on a result of the acquired classification.

https://ps.is.tuebingen.mpg.de/research_projects/semantic-optical-flow

https://fling.seas.upenn.edu/~xiaowz/dynamic/wordpress/monocap/

 

http://cs.brown.edu/~ls/Publications/SigalEncyclopediaCVdraft.pdf

 

http://www.hizook.com/blog/2010/02/16/learning-estimate-robot-motion-and-find-unexpected-objects-optical-flow

http://www.cc.gatech.edu/~dellaert/pub/Roberts09cvpr.pdf

http://www.vision.cs.ucla.edu/papers/karasevAHS16.pdf

https://arxiv.org/abs/1504.06852

https://arxiv.org/pdf/1503.04036.pdf

https://arxiv.org/abs/1702.05729

Emergency Vehicle Alerting

google-car-1

Emergency Vehicle Alerting system that warns drivers when they are approaching an ambulance, fire engine, police or rescue squad using emergency lights.
A warning system for alerting the driver of a private vehicle that an emergency vehicle is approaching is disclosed. The system includes a receiver and a display panel mounted in the private vehicle, and at least two infrared receivers mounted on the private vehicle. The display panel mounted in the private vehicle including indicating devices that allow the driver of the private vehicle to know of the approaching emergency vehicle as well as the direction to move in order to yield the right of way to an approaching emergency vehicle.

http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.399.7544&rep=rep1&type=pdf

http://patents.justia.com/patent/9278689

https://www.google.com/patents/US7057528

https://www.google.com/patents/US20110187559

http://www.google.ca/patents/US7061402

http://www.ifv.nl/kennisplein/Documents/20082-detection-of-emergency-vehicles.pdf

 

Car accident detection – sounds of crashing

hydroplaning noise https://arxiv.org/abs/1511.07035

http://karol.piczak.com/papers/Piczak2015-ESC-ConvNet.pdf

Detecting car part failures with sounds and deep learning http://www.vocativ.com/387520/artificial-intelligence-car/

http://www.cs.tut.fi/sgn/arg/music/tuomasv/parascandolo-icassp2016.pdf

PID Control – SDC Neural Network Cruise Control