Course description
Motion Detection using Python and OpenCV is a complete tutorial for step-by-step implementation of a vehicle counter and a social distance detector. Motion detection is a subfield of computer vision that aims to detect motion in videos or in real time. This type of application can be very useful especially for security systems in which suspicious movements such as a thief trying to enter the house should be identified. There are several other applications such as: highway traffic analysis, people detection/counting, animal tracking, cyclist counting, etc. A traffic control system can use these techniques to identify the number of cars and trucks crossing the highway at certain times of the day, so road maintenance can be planned.
What you will learn in this course:
- Basic understanding of background subtraction applied to motion detection
- Implementation of MOG, GMG, KNN and CNT algorithms using OpenCV and also comparing their quality and performance
- Improve the quality of the results by using pre-processing techniques such as morphological operations and blurring
- Implement a motion detector to monitor environments
- Run a social distancing tracker
- Implement car and truck counters using highway videos
Who is this course suitable for?
- People interested in implementing motion detectors or object counters
- Bachelor’s and Master’s students in computer graphics, digital image processing or artificial intelligence
- Data scientists who want to increase their knowledge in computer vision
Course specifications :
Headlines of the 2023/4 period:
Course prerequisites:
- Programming logic
- Basic Python programming
Motion Detection images using Python and OpenCV
Sample video
Installation guide
After extracting, watch with your favorite player.
Subtitle: None
Quality: 720p
download link
Password file(s): www.downloadly.ir
Size
2.57 GB
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