Fusion of Optical Flow and Histogram of Oriented Gradient Features in Abnormal Activity Recognition

Nowadays abnormal action and event recognition is very encouraging research topic in surveillance applications. There are three main component of abnormal activity recognition. The components are: (i) Feature representation approaches (ii) pattern recognition models and (iii) performance evaluation strategies. Compare to second and third component, first component: Feature representation is very important. It should have robust appearance and motion information. This information of the features plays very important role in video analysis. In this paper, fusion of features has been performed to find more accurate recognition of abnormal activity. Optical Flow and HOG (Histogram of Oriented Gradients) features has been fused to get more accurate feature vector. Upon this vector rule based recognition method has been applied for classifying abnormal event. This proposed algorithm has been implemented on UCF, BEHAVE and UMN dataset. The result shows that with fusion of features more accuracy has been achieved in finding starting frame of abnormal activity in videos. Compare to existing algorithm, proposed algorithm achieved 16%, 1.8% and 21% more accuracy in UMN, BEHAVE and UCF dataset respectively.