Automated Complex Activity Recognition in Multiple Person Interaction
Vision-based activity recognition is an essential and challenging theme in computer vision community, with various significant applications including smart video surveillance, abnormal detection system and human - computer interactions. In this paper, an activity recognition
method Cumulative Difference Intensity Distance Group Pattern (CDIDGP) based on motion information extracted from n - cumulative frame difference images. Initially, motion information is obtained by performing absolute difference between two successive frames, then difference
frame is accumulated through n - frames forming an n - cumulative frame difference images. The CDIDGP features extracted from the n - frame difference images characterize the motion vector information of the activity. The experiments are carried out using UT-Interaction dataset
(Set 1 and Set 2) consider only full-body movements of interaction between two persons and the extracted CDIDGP feature set are modeled by the various tree based classifier like Random Forest, Decision Tree (J48) and Naive Bayes. Experimental results show that this approach is effective and efficient to recognize interaction activity with an overall recognition accuracy of 90.28% and 86.13% on the UT-Interaction dataset (Set 1 and Set 2) respectively. The performance of the proposed approach outperforms previously published approaches.
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