Detection of anomalous behavior in an examination hall towards automated proctoring

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 histogram of gradient orientations followed by a nearest-neighbor 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.