About the Detection of Suspicious Human Activity in Surveillance Video from CCTV Footage project
Detection of Suspicious Human Activity in Surveillance Video from CCTV Footage applies computer vision and deep learning to CCTV footage so that unusual or unauthorised activity can be flagged automatically, instead of relying on a person watching screens for hours.
In this build: Video frames are analysed to recognise unusual or suspicious human behaviour and trigger an alert.
ProjectNet offers Detection of Suspicious Human Activity in Surveillance Video from CCTV Footage as an Artificial Intelligence final year project for B.E / B.Tech (CSE, IT, AI & DS, AI & ML), BCA, MCA, M.Tech and B.Sc Computer Science. It comes with source code, a project report, a PPT and an installation guide, so you can set it up, understand every module and explain it confidently in your review and viva.
Problem statement
Manual monitoring of surveillance video is tiring, error-prone and does not scale across many cameras; important events are often noticed only after the incident. This project analyses video frames automatically, recognises people and their behaviour and raises an alert when something suspicious happens.
Existing system vs proposed system
Existing system
Traditional CCTV systems only record video. Operators must watch live feeds or review footage later, so incidents are easily missed and response is slow.
Proposed system
The proposed system reads video frames, detects people with a deep-learning model, classifies their activity or identity status and generates an alert with the relevant frame for the operator.
Key features of Detection of Suspicious Human Activity in Surveillance Video from CCTV Footage
- Processes recorded CCTV footage or a live camera feed
- Person detection with a deep-learning model
- Classification of normal, suspicious or unauthorised behaviour
- Automatic alert with the relevant frame saved as evidence
- Operator dashboard with event history
- Adjustable detection threshold
System modules
| Module | What it does |
|---|---|
| Video input and frame extraction | Reads video files or a camera stream and extracts frames. |
| Preprocessing | Resizes frames, reduces noise and applies background subtraction. |
| Person detection | Locates people in each frame using a CNN-based detector. |
| Activity / entity classification | Classifies behaviour or checks whether the person is authorised. |
| Alert generation | Raises on-screen, log or e-mail alerts and stores evidence frames. |
| Dashboard | Shows detections, alerts and history to the operator. |
How the system works
- Video is loaded or streamed from a camera.
- Frames are extracted and preprocessed.
- A deep-learning model detects people in each frame.
- Behaviour or identity is classified.
- Suspicious events trigger an alert with a saved frame.
- The operator reviews alerts on the dashboard.
Technology stack
Typical technologies used for Artificial Intelligence projects like this one. The installation guide lists the exact versions used in the build.
| Programming language | Python 3.x |
|---|---|
| Deep learning | TensorFlow / Keras (CNN and related models) |
| Data & image processing | OpenCV, NumPy, Pandas |
| Visualisation | Matplotlib, Seaborn |
| Application layer | Flask or Django with HTML, CSS and Bootstrap |
| Development tools | VS Code, Jupyter Notebook or Google Colab |
Hardware and software requirements
| Processor | Intel Core i3 / i5 or above (GPU optional; Google Colab can be used for training) |
|---|---|
| RAM | 8 GB recommended (4 GB minimum) |
| Storage | 10 GB free space for dataset, models and libraries |
| Operating system | Windows 10/11, Ubuntu or macOS |
| Software | Python 3.8+, pip, a modern browser (Chrome / Edge) |
Testing and evaluation
Detection precision, recall, false-alarm rate and frames-per-second processing speed are the main measures.
What you get with this project
- Complete source code with comments
- Project report (abstract, literature review, design, implementation, testing, conclusion)
- PPT for your review presentation
- Installation guide to run the project step by step
- Student support through WhatsApp / phone (+91 9688163291)
To order, message ProjectNet on WhatsApp or call +91 9688163291. After you confirm and pay through GPay, PhonePe or Paytm, the files are shared with you.
Applications and future scope
Applications
- Offices, campuses and residential complexes
- Banks and ATMs
- Retail stores
- Restricted-area monitoring
Future enhancements
- Real-time multi-camera support
- Mobile push notifications
- Face recognition for access control
Viva questions for Detection of Suspicious Human Activity in Surveillance Video from CCTV Footage
Be ready to answer these in your project review:
- Why is deep learning better than simple motion detection for CCTV?
- How does a CNN detect objects in a frame?
- What preprocessing is applied to video frames?
- How do you handle false alarms in suspicious-activity detection?
- What is the difference between detection and classification?
- What are the privacy concerns of video analytics?
Who can use this project?
Detection of Suspicious Human Activity in Surveillance Video from CCTV Footage is suitable for B.E / B.Tech (CSE, IT, AI & DS, AI & ML), BCA, MCA, M.Tech and B.Sc Computer Science. ProjectNet is based in Bangalore, Karnataka and helps students looking for final year projects in Bangalore and other cities, since all files are shared digitally.
Frequently asked questions
What is included with the Detection of Suspicious Human Activity in Surveillance Video from CCTV Footage project?
Every ProjectNet project is offered with the complete source code, a project report, a PPT and an installation guide.
How can I get the Detection of Suspicious Human Activity in Surveillance Video from CCTV Footage project?
Contact ProjectNet on WhatsApp at +91 9688163291 or call 9688163291 / 8270827832. After you confirm the project and make the payment through GPay, PhonePe or Paytm, the source code and report are shared with you.
What is the price of the Detection of Suspicious Human Activity in Surveillance Video from CCTV Footage project?
Please contact ProjectNet on WhatsApp for the current price of this project.
Which students can use the Detection of Suspicious Human Activity in Surveillance Video from CCTV Footage project?
It is suitable for B.E / B.Tech (CSE, IT, AI & DS, AI & ML), BCA, MCA, M.Tech and B.Sc Computer Science. Use it as a reference to understand, learn from and extend.
Which technologies and software are needed for Detection of Suspicious Human Activity in Surveillance Video from CCTV Footage?
The typical stack is Python 3.x, TensorFlow / Keras (CNN and related models), OpenCV, NumPy, Pandas, Matplotlib, Seaborn. The installation guide lists the exact versions and steps.
Can Detection of Suspicious Human Activity in Surveillance Video from CCTV Footage work on a live camera?
Surveillance projects normally work on recorded video files and can be extended to a webcam or IP-camera stream. Ask the team which input modes are supported in this build.