1. Using custom compiled opencv-contrib library with OPENMP enabled

   open mp will enable pararell processing, which will allow us to utilize all four core in our raspi, but somehow as mentioned here, the improvement not really significant, but still worth to try

   really usefull links

   • https://stackoverflow.com/questions/29494503/how-to-compile-opencv-with-openmp
   • http://answers.opencv.org/question/103701/how-opencv-use-openmp-thread-to-get-performance/

2. Research about the encoding that being used in the realtime video processing

   if you still using usb web cam, while based on the previous experience, seems not very bad, the problem with the usb webcam is that the encoding process of the realtime video will hogging the cpu so much by theory (but somehow seems not really happening), rather than if you're using raspi camera which will utilize the GPU to do the encoding, so probably this also worth to research

3. MUST Buy Raspi CAM

   as i mention earlier, this also worth to try, also lot of people mention about using good driver for raspi cam, also research about that

   The Pi camera is 'run' by the GPU and can dump full frames into RAM at 15 frames a second .. this is 7.5MB/frame, 15fps = 112.5 Mega BYTES per second .. or you can have full HD resolution 30fps H264 encoded (by the GPU) along with some simultaneous still photos (Google>MMAL) all at virtually zero CPU loading ..

   On the other hand, the Pi USB is 'run' byte at a time by the CPU, and, at the cost of 100% CPU loading you might achieve a couple of hundred Mega BITS per second .. not that a web cam is going to deliver that anyway (even if it could, you then don't have any CPU cycles to do anything with it ..)

   SO, unless CCTV resolutions (320x240 pixels) are what you want, it has to be the Pi camera.

   Reference links

   • https://www.raspberrypi.org/forums/viewtopic.php?t=221425
   • https://www.raspberrypi.org/forums/viewtopic.php?t=207554
   • https://www.raspberrypi.org/forums/viewtopic.php?t=116828
   • https://raspberrypi.stackexchange.com/questions/48032/advantages-of-raspberry-pi-15-pin-mipi-camera-interface-csi-connector-cameras

4. Update raspi to last version

   uselfull links - Raspbian strecth

5. Update your code to fit with OpenCV 4

   opencv 4 as mentioned in its website offers more feature, and some new algorithm, not really mentioning about performance increase, but we're expecting there is no performance degradation, so this also worth to try

   Usefull links

   • https://www.pyimagesearch.com/2018/09/26/install-opencv-4-on-your-raspberry-pi/

6. Reseach about designing the code that fits into CPU Cache

   Good code will utilize the cpu cache and expected to be less likely to dumping the preprocessed data into ram (or even secondary memory) so this also worth to try

   Reference links

   • https://stackoverflow.com/questions/763262/how-does-one-write-code-that-best-utilizes-the-cpu-cache-to-improve-performance
   • https://stackoverflow.com/questions/1822295/design-code-to-fit-in-cpu-cache

7. Multithreading the code

   (perhaps) i already implemented this by the time you reading this, but still worth to research if any better library than imutils WebcamVideoStream

   Usefull links

   • https://www.pyimagesearch.com/2015/12/21/increasing-webcam-fps-with-python-and-opencv/
   • https://www.pyimagesearch.com/2015/12/28/increasing-raspberry-pi-fps-with-python-and-opencv/
   • https://www.pyimagesearch.com/2016/01/04/unifying-picamera-and-cv2-videocapture-into-a-single-class-with-opencv/

   ps : imho pyimageresearch still the best for providing topic about opencv, makesure you check it

8. Add argument parser | #TODO

   For every script that need to run on the match Sure helps for debuging (eg: need display, add different parameter, etc) and when playing, just don't include the argument

9. Make a proper code documentation

   make other people get what you code

   Usefull links

   • https://realpython.com/documenting-python-code/

10. Researching for another object detection method

    Our current method is only using hsv color filtering which also appear to be the lightest method possible, but in some cases it is not robust enough, eg when the environtment not contrast enough (like the ball cases) for the goal post detection (the ball detection working fine so far). For our raspi system, perhaps its still possible to use more processing power since our current method barely utilize 30% cpu usage.

    Tried method

    • SIFT SURF ORB and backprojection barely detect the object, very heavy the script is in /OldScript/feature_detection_SIFT_SURF_ORB.py try again if you courious
    • Homography detecting the object, very heavy even on my laptop
    • Haar Cascade should be working, but i've tried and seems the dataset kinda bad, also the positive image also not very good quality there is separate repository for it here

    Not tried

    • Using YOLO object detection cons : Very very cpu intensive, using some kind of deep learning algorithm

    • Histogram of object oriented gradient (advice from our prof)

      • https://www.pyimagesearch.com/2014/11/10/histogram-oriented-gradients-object-detection/
      • https://www.pyimagesearch.com/2015/11/16/hog-detectmultiscale-parameters-explained/

    problem

    • the haar cascade method rely on the result of image training, in this case i've tried to train a goal post image from internet, but not seems to be working in the field, in this case its not possible to re training with the goal post image taken before the match, but i mention earlier about the bad datasets that i used, so worth to retry this method again using good datasets.
    • the YOLO image seems more reliable because of its fancy deep learning, but again its seems not possible on our raspi system or other small single board computer, but essetialy its also need to train the image and IF for getting reliable image, wwe should take the datasets image when match trial session, then seems also not possible
    • the other sift surf orb, backprojection, homograpy is also rely on image feature matching, eg the goal post, and its possible to take the photo when match trial session

11. Research about ROS

With our current method to drive the servo and stuff, as far as i know, it's fundamentally different than how ros works. And so little reference about how to drive non dynamixel servo, and "really" builing at least biped robot. So this part perhaps need longer time to research than any other part. Carefully read the whole documentation from ros website (link below)

Usefull Reference

• https://answers.ros.org/question/270089/how-to-implement-ros-in-my-custom-made-robot/
• http://wiki.ros.org/tf/Tutorials

12. Research Distance using Stereo Camera
    Keywords : Angle + Triangulation Usefull reference - https://www.pyimagesearch.com/2015/01/19/find-distance-camera-objectmarker-using-python-opencv/ - https://photo.stackexchange.com/questions/12434/how-do-i-calculate-the-distance-of-an-object-in-a-photo - https://www.pyimagesearch.com/2016/04/04/measuring-distance-between-objects-in-an-image-with-opencv/

13. Research Deep Learning Based Image recognition
    - DNN - Yolo