Suspicious Human Activity Recognition using Statistical Features

Abstract

This paper presents a new algorithm for suspicious human activity recognition in videos based on a combination of two different feature types. The first feature concerns the shape and is called shape moments. The second concerns the boundary coordinates and is called "Histogram of Normalized Distances (HND) from Center of gravity of the object shape (COG) and it's contour points" combining these features leads to the formation of a strong complementary feature vector that captures effective discriminate details of human action videos. The authors used two methods for classification, the Multi-class Support Vector Machine and Naive Bayes classifier. The classification by using the Multi-class SVM classifier verified recognition rate up to 95.6 %, but the Naive Bayes classifier verified 97.2%. The authors evaluated the suspicious activity recognition on 250 videos from HMDB data set. Five distinct suspicious human activities (e.g., Running, Punching, Kicking, Shooting guns and Falling floor, etc.) by 250 different persons. Experiments on HMDB show that the presented system can recognize suspicious activities effectively and accurately in surveillance videos.