Motion Detection OpenCV Python With Source Code (2026)

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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Download Source Code below

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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.

Inquiries

If you have any questions or suggestions about Motion Detection OpenCV Python With Source Code, please feel free to leave a comment below.

Technology stack and requirements

To run this Python project on your development machine, you need:

Python 3.10 or higher. Download from python.org or install via Anaconda if you prefer bundled packages.
pip package manager. Comes with Python. Used to install project dependencies from requirements.txt.
Virtual environment. Use venv or conda to isolate project dependencies from your global Python install.
VS Code or PyCharm. Free code editors with Python syntax highlighting, IntelliSense, and debugging.
Git. For version control and cloning source code repositories.

Installing the source code

Download or clone the repository. Get the ZIP archive from the download link on this page and extract it.
Create a virtual environment. Open a terminal in the project folder and run: python -m venv venv, then activate it (venv\Scripts\activate on Windows or source venv/bin/activate on Mac/Linux).
Install dependencies. Run pip install -r requirements.txt to install all libraries the project needs.
Configure environment variables. If the project uses API keys (OpenAI, Anthropic, database), create a .env file and set the required keys.
Run the project. Follow the run command in the README (usually python main.py or streamlit run app.py).

Using this project for your BSIT capstone

Chapter 1 (Introduction). Discuss the real-world problem this system solves. Cite Philippine or international use cases where the manual process could be automated.
Chapter 2 (RRL). Compare your project against 5-10 similar published works. Cite ACM, IEEE, or arXiv papers for academic-standard sources.
Chapter 3 (Methodology). Document the model architecture, training data, hyperparameters, and evaluation metrics used.
Chapter 4 (Results). Report accuracy, precision, recall, F1-score, and confusion matrix. Screenshot the running app on real inputs.
Chapter 5 (Conclusion). Identify features for Version 2: better model, larger dataset, mobile deployment, or REST API.

Modules typical of Motion Detection OpenCV Python

Image dataset preparation. Train/val/test split, augmentation (rotation, flip, brightness).
Model architecture. Pre-trained CNN (ResNet, EfficientNet) fine-tuned on your dataset.
Training loop. PyTorch or TensorFlow/Keras training with early stopping and checkpoints.
Evaluation. Confusion matrix, precision, recall, and per-class F1 scores.
Inference API. FastAPI endpoint accepting image uploads and returning predictions.
Web or mobile demo. Streamlit or React front-end for user interaction.

Common enhancements for capstone review

Grad-CAM visualization. Show which pixels the model focused on when making a prediction.
Data augmentation. Increase training data variety with imgaug or Albumentations library.
Transfer learning benchmark. Compare training-from-scratch vs fine-tuning pre-trained models.
Model quantization. Convert to TensorFlow Lite for mobile deployment.

Official documentation

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.

Angel Jude Suarez

Full-Stack Developer at PIES IT Solution

Focuses on Python development, machine learning, and AI integration. Has built production AI systems including OpenAI Whisper integration for medical transcription and GPT-4o-powered diagnosis assistance. Strong background in pandas, scikit-learn, and TensorFlow.

Expertise: Python · PHP · Java · VB.NET · ASP.NET · Machine Learning · AI Integration · OpenCV · Django · CodeIgniter
· View all posts by Angel Jude Suarez →

Motion Detection OpenCV Python With Source Code