UberPoC is an autonomous software able to detect lines on the ground, detect signs, traffic lights and pedestrians.
$ git clone https://github.com/PoCFrance/UberPoC
$ cd UberPoc
pygletimutilsnumpymatplotlibopencv-contrib-pythontensorflowscikit-learnkerasbeautifulsoup4
If you want to try Line Detection System through a video or the Duckietown Simulator :
$ ./run.py --video-name [video.mp4]
or
$ ./run.py --duckietown
You can press L to toggle line detection
You can also use N to see different step of normalization
If you want to try Sign Detection over an image or your Camera:
$ ./run.py sign_detection --cam
or
$ ./run.py sign_detection --img [img.png]
To try Human Detection over an image:
$ ./run.py human_detection --img [img.png]
- Line Detection : Displays the lines on the ground while the car is driving.
- Pedestrian Detection: Circle the pedestrians and cyclists on an image.
- Detection and recognition of road signs : Circle and name traffic signs
- Auto pilot : Combine all the features in order to have an autonomous car
- More road signs and traffic lights : Find dataset of others signs and traffic lights to train AI
- We cut the top of the image in order to gain precision by having only the bottom with the lines
- Then we apply a red filter on the image to better differentiate the white lines from the rest of the image
- Using the Canny function of OpenCV, the lines are cut
- Using the HoughLinesP function we get an array with the different points that make up the lines
- With these arrays the lines are estimated and displayed on the screen
- To detect the pedestrians on a road we use a function of OpenCV (HOGDescriptor_getDefaultPeopleDetector()) who get all the regions of each person on an image
- Then we iterate on these regions and display a rectangle around each pedestrian
- To detect the different traffic signs present on an image or video, we use an AI based on a particular model the "Faster R-CNN" (Region-based Convolutional Neural Networks)
- AI will scan the full image of small regions to be able to detect a sign on the image
- Once the sign is found it will identify it and then surround it on the image with its accuracy rate and type