Multimodal decomposable models by superpixel segmentation and point-in-time cheating detection

Abstract

This research aims to classify cheating activity during exam from video observation. The method uses Conditional Random Field (CRF) for classifying and detecting some classes of cheating activities. The method used to detect the location of the joints of the body is a Multimodal Decomposable Model (MODEC) with superpixel segmentation. The used joints are head, shoulders, elbows, and wrists. The superpixel method is Simple Linear Iterative Clustering (SLIC). Comparison between MODEC and MODEC + SLIC as feature detector for CRF showed that MODEC + SLIC capable of providing a better activity classification. From our experiments, the cheating activities in average can be detected up to 83.9%. Moving beyond only detecting the class of motion segments, we also devised point-in-time event detection system also using CRF. The time of occurrences of three consecutive cheating activities are determined from a sequence of video frames.