Motion Detection OpenCV Python With Source Code
The Motion Detection OpenCV Python was developed using Python OpenCV , This Project is used in CCTV Cameras to detect any kind of motion in the video frame.
In this blog, we are going to make a motion detection script using OpenCV in Python.
A Motion Detection OpenCV Python Algorithm Capture Video, in which you have to detect movement using OpenCV in Python.
In many applications based on machine vision, motion detection is used.
For example, when we want to count the people who pass by a certain place.
In all these cases, the first thing we have to do is extract the people that are at the scene.
What is OpenCV?
OpenCV (Open Source Computer Vision Library) is an open-source computer vision and machine learning software library.
OpenCV was built to provide a common infrastructure for computer vision applications and to accelerate the use of machine perception in commercial products.
In this Python OpenCV Project also includes a downloadable Python Project With Source Code for free, just find the downloadable source code below and click to start downloading.
By the way if you are new to python programming and you don’t know what would be the Python IDE to use, I have here a list of Best Python IDE for Windows, Linux, Mac OS that will suit for you.
I also have here How to Download and Install Latest Version of Python on Windows.
To start executing Motion Detection OpenCV Python With Source Code, make sure that you have installed Python 3.9 and PyCharm in your computer.
Motion Detection OpenCV Python With Source Code: Steps on how to run the project
Time needed: 5 minutes
These are the steps on how to run Motion Detection OpenCV Python With Source Code
- Step 1: Download the given source code below.
First, download the given source code below and unzip the source code.
- Step 2: Import the project to your PyCharm IDE.
Next, import the source code you’ve download to your PyCharm IDE.
- Step 3: Run the project.
last, run the project with the command “py main.py”
Installed Libraries
import cv2 as cv import numpy as np from matplotlib import pyplot as plt
Complete Source Code
import cv2 as cv
import numpy as np
from matplotlib import pyplot as plt
def motionDetection():
cap = cv.VideoCapture("./img/vtest.avi")
ret, frame1 = cap.read()
ret, frame2 = cap.read()
while cap.isOpened():
diff = cv.absdiff(frame1, frame2)
diff_gray = cv.cvtColor(diff, cv.COLOR_BGR2GRAY)
blur = cv.GaussianBlur(diff_gray, (5, 5), 0)
_, thresh = cv.threshold(blur, 20, 255, cv.THRESH_BINARY)
dilated = cv.dilate(thresh, None, iterations=3)
contours, _ = cv.findContours(
dilated, cv.RETR_TREE, cv.CHAIN_APPROX_SIMPLE)
for contour in contours:
(x, y, w, h) = cv.boundingRect(contour)
if cv.contourArea(contour) < 900:
continue
cv.rectangle(frame1, (x, y), (x+w, y+h), (0, 255, 0), 2)
cv.putText(frame1, "Status: {}".format('Movement'), (10, 20), cv.FONT_HERSHEY_SIMPLEX,
1, (255, 0, 0), 3)
# cv.drawContours(frame1, contours, -1, (0, 255, 0), 2)
cv.imshow("Video", frame1)
frame1 = frame2
ret, frame2 = cap.read()
if cv.waitKey(50) == 27:
break
cap.release()
cv.destroyAllWindows()
if __name__ == "__main__":
motionDetection()
Output
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Anyway, if you want to level up your programming knowledge, especially Python OpenCV, try this new article I’ve made for you Best OpenCV Projects With Source Code For Beginners.
Summary
In many applications based on machine vision, motion detection is used.
For example, when we want to count the people who pass by a certain place or how many cars have passed through a toll.
In all these cases, the first thing we have to do is extract the people or vehicles that are at the scene.
There are different techniques, methods, or algorithms that enable motion detection.
As in other subjects, there are no generic cases in artificial vision.
It will depend on each situation to use one or the other. Let us have a look at some methods used in OpenCV and Computer Vision.
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Inquiries
If you have any questions or suggestions about Motion Detection OpenCV Python With Source Code, please feel free to leave a comment below.
Frequently Asked Questions
How does motion detection work in OpenCV?
Frame differencing: subtract the current frame from the previous frame (or a longer background model via cv2.createBackgroundSubtractorMOG2). Pixels that changed significantly are flagged as motion. Threshold + dilate + find contours to get bounding boxes around moving objects. Useful for security cameras, wildlife monitoring, and triggering recordings only when something happens.
What Python and library versions do I need?
Python 3.10, 3.11, or 3.12 (avoid 3.13 until all DL wheels catch up). Install with: pip install opencv-python numpy. For deep learning models add: tensorflow keras (CPU build is fine for most demos), torch torchvision (PyTorch alternative), mediapipe (for face/hand/pose). Some projects also need: pytesseract for OCR, pyzbar for barcode, dlib for legacy face-landmark predictor.
Do I need a GPU to run this deep learning project?
For inference on a pretrained model: no, CPU runs at 10-30 FPS for most computer-vision tasks. For TRAINING a custom model: GPU strongly recommended (CPU works but slow). Free GPU options for training: Google Colab Free (12-hour sessions, sufficient for most BSIT capstones), Kaggle Notebooks Free. Buying a $1000+ GPU just for capstone is overkill.
Can I use this deep learning project for a BSIT or CSE capstone?
Yes, but extend it. A single OpenCV deep-learning demo (face detection, object detection alone) is too narrow for full capstone scope. Combine with a real domain wrapper: an attendance system using face recognition, a traffic monitoring system using vehicle detection, a wildlife camera using object detection, a driver-monitoring app using drowsiness detection. Add database logging, simple UI, and Chapter 1-5 manuscript.
Why does my model give wrong predictions or low accuracy?
Three most common causes: (1) Input preprocessing mismatch: the model expects 224×224 RGB normalized to [0,1] or [-1,1]; using BGR (OpenCV default) or wrong size produces garbage. (2) Insufficient training data: if you trained your own model on under 1,000 samples per class, accuracy plateaus low. Augment with cv2.flip, rotate, brightness shifts. (3) Lighting and angle drift between training and live use: train on data that matches the deployment environment.
Where can I find more deep learning project ideas with source code?
Browse our Deep Learning Projects hub for 19+ vision demos. For broader AI / ML / RAG / NLP capstones see 100+ AI Capstone Project Ideas. For pure ML (no deep learning) see Machine Learning Projects.