This work is an extension of the project https://github.com/ablanco1950/LicensePlate_Yolov8_Filters_PaddleOCR adding the possibility to detect the speed, tracking and counting cars
The requirements are exactly the same as those indicated in the aforementioned project.
Downloaded the project, execute the pythom program
VIDEODetectSpeed_and_ Counter_LicensePlate_Yolov8_Filters_PaddleOCR.py
The test program VIDEODetectSpeed_and_ Counter_LicensePlate_Yolov8_Filters_PaddleOCR.py is only prepared to work with the attached Traffic IP Camera video.mp4 test video, dowloaded from https://github.com/anmspro/Traffic-Signal-Violation-Detection-System/tree/master/Resources, since speed detection is performed over a region of the video, marked with a green rectangle, whose depth coincides with the length of a parking space that appears in the video and according to the following formula and parameters:
"""
fps=25 #frames per second of video, see its properties
fpsReal= fps/SpeedUpFrames # To speed up the process only one of SpeedUpFrames
# is considered, SpeedUpFrames=5
lengthRegion=4.5 #the depth of the considered region corresponds
# to the length of a parking space which is usually 4.5m
Snapshots detected in the video region
Speed (Km/hour)=lenthRegion * fpsReal * 3.6 / Snapshots
Where 3.6 = (3600 sec./ 1 hour) * (1Km/ 1000m)
This formula depends on the number of snapshots detected, which depends on the quality and speed of the plate detector, so in any case it has to be adjusted with practical tests in the field.
As a result, the console gets the following output:
AR606L Speed: 27.0Km/h snapshots: 3
AE670S Speed: 40.5Km/h snapshots: 2
APHI88 Speed: 81.0Km/h snapshots: 1
A3K96 Speed: 40.5Km/h snapshots: 2
A968B6 Speed: 40.5Km/h snapshots: 2
AV6190 Speed: 27.0Km/h snapshots: 3
In which it is verified that the speed is determined by the number of snapshots in the delimited region of interest. There is one error from false detections of plate A3K961 that is detected as A3k96
Adjustments would be necessary with real and verifiable cases.
A camera with more frames per second is needed, a computer with better features and better license plate detection.
You also get a logging file VIDEOLicenseResults.txt with the detected license plates
and a summary file: VIDEOLicenseSummary.txt with the following fields:
- License detected
- number of snapshots in the region of interest
- time of first snapshot
- last snapshot time
- estimated speed
And the car`s counter
The main advantage of this method is that it seems to be insensitive to the load that the computer that executes it is supporting, which allows the observed values to be adjusted.
The same thing doesn't seem to happen when running:
SpeedEstimationUltralytics.py
copied from https://docs.ultralytics.com/guides/speed-estimation/
in which the speeds vary at different execution times of the program.
At https://www.ultralytics.com/es/blog/ultralytics-yolov8-for-speed-estimation-in-computer-vision-projects It is indicated that the results depend on the speed of the GPU
With respect the tracking method, depends directly on the detection of the license plate. It is more precise but produces errors if the license plate is detected incorrectly. You can compare the results with those obtained with the tracking functions incorporated in the new ultralytics versions by running: TestCounterUltralytics.py
For that you must have an upgraded version of ultralytics and the proper version of lap
inside conda in the scripts directory of the user environment
python pip-script.py install --no-cache-dir "lapx>=0.5.2"
upgrade ultralytics
python pip-script.py install --upgrade ultralytics
To see the results it is better to watch the output video
object_counting_output.avi
A test has also been performed using sahi prediction integrated with yolov8
(https://docs.ultralytics.com/es/guides/sahi-tiled-inference/)
Which can be verified by running:
(You need to have installed sahi: pip install -U ultralytics sahi )
VIDEODetectSpeed_and_ Counter_LicensePlate_SahiYolov8_Filters_PaddleOCR.py
The results, barring error or omission, are worse
It is expected to improve the results in subsequent versions by applying the specifications that appear in https://blog.roboflow.com/estimate-speed-computer-vision/ and others
References:
https://github.com/amanraja/vehicle-speed-detection-
https://www.ijcseonline.org/pub_paper/124-IJCSE-06271.pdf
https://medium.com/@raja_8462/an-efficient-approach-for-vehicle-speed-detection-1fce82aacaf2
https://pypi.org/project/paddleocr/
https://learnopencv.com/ultralytics-yolov8/#How-to-Use-YOLOv8?
https://public.roboflow.com/object-detection/license-plates-us-eu/3
https://docs.ultralytics.com/python/
https://medium.com/@alimustoofaa/how-to-load-model-yolov8-onnx-cv2-dnn-3e176cde16e6
https://machinelearningprojects.net/number-plate-detection-using-yolov7/
https://github.com/ablanco1950/LicensePlate_Yolov8_MaxFilters
https://docs.ultralytics.com/guides/speed-estimation/
https://blog.roboflow.com/estimate-speed-computer-vision/
citations and thanks:
@article{akyon2022sahi, title={Slicing Aided Hyper Inference and Fine-tuning for Small Object Detection}, author={Akyon, Fatih Cagatay and Altinuc, Sinan Onur and Temizel, Alptekin}, journal={2022 IEEE International Conference on Image Processing (ICIP)}, doi={10.1109/ICIP46576.2022.9897990}, pages={966-970}, year={2022} }
Tracking and Counting:
https://github.com/pderrenger/pderrenger
https://github.com/pderrenger?tab=repositories
https://github.com/orgs/ultralytics/discussions/8112 (example from pderrenger)
https://towardsdatascience.com/mastering-object-counting-in-videos-3d49a9230bd2
https://docs.ultralytics.com/es/guides/sahi-tiled-inference/
Filters:
https://gist.github.com/endolith/334196bac1cac45a4893#
https://stackoverflow.com/questions/46084476/radon-transformation-in-python
https://gist.github.com/endolith/255291#file-parabolic-py
https://learnopencv.com/otsu-thresholding-with-opencv/
https://towardsdatascience.com/image-enhancement-techniques-using-opencv-and-python-9191d5c30d45
https://blog.katastros.com/a?ID=01800-4bf623a1-3917-4d54-9b6a-775331ebaf05
https://programmerclick.com/article/89421544914/
https://anishgupta1005.medium.com/building-an-optical-character-recognizer-in-python-bbd09edfe438
https://datasmarts.net/es/como-usar-el-detector-de-puntos-clave-mser-en-opencv/
https://felipemeganha.medium.com/detecting-handwriting-regions-with-opencv-and-python-ff0b1050aa4e
https://github.com/victorgzv/Lighting-correction-with-OpenCV
https://medium.com/@yyuanli19/using-mnist-to-visualize-basic-conv-filtering-95d24679643e