Clustering Textures with EHG Algorithm for Modelling Video

In this paper we present a novel approach for common recognition of group activities for video surveillance applications. We propose a Energetic-based approach for detecting abnormal events in surveillance video. It requires the appropriate definition of similarity between events. Human pose estimation via motion tracking systems can be considered as a regression problem within a discriminative framework. We defined the overfitting problem was handled by Hidden Markov Model based similarity. We propose in this paper a multi model-based similarity measure. In this measure, the Hidden Markov Model training and distance measuring are based on multiple samples. The novel Energetic Hierarchical Group (EHG) method acquired the multiple training data. By iteratively reclassifying and retraining the data groups at different clustering levels, the initial training and clustering errors due to overfitting will be sequentially corrected in later steps. Experimental results on real surveillance video show an improvement of the proposed method over a stand column method that uses single sample- based similarity measure and spectral clustering