A Review of Computational Approaches for Human Behavior Detection

Abstract

Computer vision techniques capable of detecting human behavior are gaining interest. Several researchers have provided their review on behavior detection, however most of the reviews are focused on activity recognition only, and reviews on gesture and facial expression recognition are very few. Therefore, all of them lack to cover complete human behavior analysis. In this study, we provide a comprehensive review of human behavior detection approaches. The framework of this review is based on activity, gesture and facial expression recognition since these are the most important cues for behavior detection. These three areas are further classified in existing computational approaches. One can easily recognize from this review that hidden Markov model is widely exploited for activity recognition while motion history image is still a developing area. Haar-like features can be a valid alternative for gesture recognition. For facial expression recognition, local binary patterns feature is a very popular choice. We have reviewed behavior detection techniques, mostly developed after year 2009. The explicit advantages of this review are: (1) it provides a deep analysis of computational approaches for activity, gesture and facial expression recognition. (2) It includes both types of techniques that include single human as well as multiple human activities. (3) It considers techniques developed in the last decade only pertaining to information about the most recent techniques. (4) It provides a brief description of popular datasets used for activity, gesture and facial expression recognition. (5) It discusses open issues to provide an insight for future also.