Compressed domain human motion recognition using motion history information

In this paper we present a system for classifying various human actions in compressed domain video framework. We introduce the notion of quantifying the motion involved, through what we call "motion flow history" (MFH). The encoded motion information readily available in the compressed MPEG stream is used to construct the coarse motion history image (MHI) and the corresponding MFH. The features extracted from the static MHI and MFH compactly characterize the temporal and motion information of the action. Since the features are extracted from the partially decoded sparse motion data, the computational load is minimized to a great extent. The extracted features are used to train the KNN, neural network, SVM and the Bayes classifiers for recognizing a set of seven human actions. Experimental results show that the proposed method efficiently recognizes the set of actions considered.