In an examination hall, invigilators are instructed to perform a variety of duties, the most important duty is to maintain discipline and make sure the examinees do not cheat. In this age where computers are now able to perceive and analyze their environment with a high degree of accuracy, we can us
Smart Invigilator
In an examination hall, invigilators are instructed to perform a variety of duties, the most important duty is to maintain discipline and make sure the examinees do not cheat. In this age where computers are now able to perceive and analyze their environment with a high degree of accuracy, we can use this advancement to automate the process of invigilation.
Smart Invigilator will be used by the person in charge of the examination hall and activated at the start of the exam. Cameras would be installed in the examination hall and they will stream the video to the system at backend, which will analyze the stream, frame by frame, and it will detect the examinees in the frame. Furthermore, it will monitor the movement of examinees in the video stream and detect suspicious activities. Finally, the suspicious movements will be reported to the head invigilator on his/her mobile.
The objectives are listed below:
The implementation consists of the following steps:
Each process is discussed below in detail.
In this phase detailed literature review will be conducted on face detection as well as movement detection to gather information about the existing work. Furthermore, the aim would be to find existing techniques and technologies that may help in the project. Literature with the latest publication (2010-2021) is planned to be concatenated but classes are also being explored.
Data Collection is considered as the most challenging task in conducting a research. This research is based on real time face detection and motion recognition in a controlled environment. For gathering data, the humanoid action frames would be captured that are suspicious and non-suspicious during an exam. Face detection algorithm would be used to detect face and extract the region of interest in rectangular bounding box. All extracted images will be converted to grayscale and resized equally. Afterwards, the dataset would be used to train the system model.
At initial stage, the Viola Jones algorithm would run on the dataset to identify the suspicious and non-suspicious haar-like features and creating a boosted classifier cascade. Afterwards, the Cascade Classifier would be used for the recognition of suspicious features based on the generated haar-cascade feature list. If a suspicious feature is recognized, the head invigilator would be notified.
The system would be thoroughly tested for inconsistencies, the testing would, first, be conducted on test data i.e., recorded video, and when the testing on recorded video is successful, the system would then be tested using a live video feed. The reliability of the system would be evaluated by the accuracy of face detection and motion recognition.
The benefits of the project are listed below:
| Item Name | Type | No. of Units | Per Unit Cost (in Rs) | Total (in Rs) |
|---|---|---|---|---|
| Camera | Equipment | 4 | 10000 | 40000 |
| 4 Channel DVR | Equipment | 1 | 10000 | 10000 |
| Tripod | Equipment | 4 | 2500 | 10000 |
| Cables & Connectors | Equipment | 1 | 5000 | 5000 |
| CV In Python! Face Detection & Image Processing (Udemy Discounted) | Miscellaneous | 1 | 2200 | 2200 |
| Python DIP From Ground Up™ (Udemy Discounted) | Miscellaneous | 1 | 2200 | 2200 |
| Complete Guide to TensorFlow for DL with Python (Udemy Discounted) | Miscellaneous | 1 | 2300 | 2300 |
| The Complete React Native + Hooks Course (Udemy Discounted) | Miscellaneous | 1 | 3200 | 3200 |
| Total in (Rs) | 74900 |
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