IntellCam
The idea of this project is to automate class vigilance process. The system will use a camera which will provide live video streaming and an alert will be generated to the corresponding authorities if the class is not being conducted. The System will also enable the authorities to identify where
2025-06-28 16:33:15 - Adil Khan
IntellCam
Project Area of Specialization Artificial IntelligenceProject Summary Project overviewThe idea of this project is to automate class vigilance process. The system will use a camera which will provide live video streaming and an alert will be generated to the corresponding authorities if the class is not being conducted. The System will also enable the authorities to identify where a particular teacher is taking a class. This will ease the manual surveillance process and makes the work to be performed more efficiently and effectively.
Project ObjectivesThis project will mainly focus on the following objectives:
- Develop a system that will generate alert or notification if the class is not being conducted according to the schedule and will be displayed on the screen.
- Also which class is busy with which particular teacher, will also be displayed on the monitoring screen.
- Utilize machine learning and image processing techniques to identify the class environment.
- Provide a more effective and efficient way for class surveillance and to generate alerts or notifications to the corresponding authorities.
Data Gathering : We will simply create datasets of images for classes being conducted, not conducted and teacher’s images from different angles.
Detection:
- The system will capture images of a particular room in which class is being conducted and compare it with the images stored in datasets.
- The most common way to detect a room in which a class is being conducted or not and also which teacher is present in this class by using "Haar Cascade classifier"
- It is a machine learning based approach where a cascade function is trained from a lot of positive and negative images. It is then used to detect room in other images.
- Initially, the algorithm needs a lot of positive images (images for class conduct) and negative images (images for class will not conduct) to train the classifier
Recognition
- The system would feed the rooms and teachers image data and respective ids of each room to the recognizer so that it can learn
- Through live streaming real time images of a class will be matched with the images stored in the dataset to determine whether a class is being conducted or not and which teacher is in the class
Notifications
- A notification would be sent to admin through firebase approach about classes not being conducted.
- Also at one corner of the monitoring screen, the status of the class and which teacher is currently in that class will be displayed.
- Reduce human resources
- Time saving
- Proper scheduling
- Timely alerts
- IP Camera
- EmguCV
- C Sharp
- For building app using hybrid technology we use either “React Native or phonegap”
- Sending notification through firebase
.
| Product | ||
| IP Cameras : DLINK DCS 6915 20X Full HD WDR |
Specification of the camera:
- Sony Exmor 1/2.8" 3 Megapixel CMOS Sensor
- Variable Speed 20x Optical Zoom
- Day & Night with Mechanical IR Filter
- 0.1 Lux (Color); 0.01 Lux (B/W)
- Electronic shutter speed 1/1 - 1/10,100 second
- Wide Dynamic Range (WDR)
- Focal Length: 4.7 to 96 mm
- Horizontal Field of View: 52° to 4°
Product
IP Cameras :
DLINK DCS 6915 20X Full HD WDR
Final Deliverable of the Project HW/SW integrated systemType of Industry IT , Security Technologies Artificial Intelligence(AI), OthersSustainable Development Goals Industry, Innovation and InfrastructureRequired Resources| Elapsed time since start of the project | Milestone | Deliverable |
|---|---|---|
| Month 1 | Project planning | Work plan |
| Month 2 | Software Requirement Specification | Software Requirement Specification |
| Month 3 | Prototype | Digital prototype of the project describing the scenario. |
| Month 4 | Implementation Phase | Teachers,Class Conduction and Un-Conduction Datasets |
| Month 5 | Implementation Phase | Image Detection App |
| Month 6 | Implementation Phase | Image Recognition App |
| Month 7 | Implementation and Integration Phase | Notification generations Code and Integration of the work. |