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The paper proposes a workflow for the automatic detection of anomalous behavior in an examination hall, towards the automated proctoring of tests in classes. Certain assumptions about normal behavior in the context of proctoring exams are made. Anomalies are behavior patterns that are relatively (and significantly) different. While not every anomalous behavior may be cause for suspicion, the system is designed to detection typical patterns for actions of concern such as discussions during an exam or the turning around or the passing of notes, etc. This detection is based on features computed using the textural features followed by a classifier search through annotated patterns of pre- recorded clips to train the system for behavior that may cause concern. While there may be false positives, the system is intended as a decision support system to facilitate automatic proctoring of tests and deters malpractice. We have discussed about various video analytics or video processing and image processing methods and tools involved in surveillance model. We throughout the paper have walk through about the various processes- preprocessing segmentation, classification, feature extraction and its related video processing algorithm in sequential manner. The proposed model is effective, efficient and requires relatively less processing power.
The major problem that occurs in examination system is malpractices. This is identified due to the absence of credible identity verification system for offline and also for online examinations. In order to overcome the above problem researchers have focused on the use of artificial techniques and use of biometrics. In the past history work has been carried out on examination malpractices.  Examination malpractice is any form of an illegal act committed during the examination. There are different forms of examination malpractice including copying from another student?s test, getting notes to examination, plagiarism and impersonating another student during a test. All these scenarios and many others give students an unfair advantage. There may be many factors that cause examination malpractice like physiological factors, societal value system, over emphasis on paper qualification and poor learning facilities.
- Fuzzy clustering
- Otsu method
- KNN classifier algorithmEXISTING DRAWBACKS
- Approximate result at the regulation of speed and direction
- It cannot work on the problems of scattering and at non co-ordinate system
- Haar-Features Algorithm
- AdaBoost Algorithm
- Principal Component Analysis(PCA)
- Concept is easy to understand
- optimization Good for ?noisy? environments
- It support multi objectiveSOFTWARE REQUIREMENTS