A model-based conceptual clustering of moving objects in video surveillance

Abstract

Data mining techniques have been applied in video databases to identify various patterns or groups. Clustering analysis is used to find the patterns and groups of moving objects in video surveillance systems. Most existing methods for the clustering focus on finding the optimum of overall partitioning. However, these approaches cannot provide meaningful descriptions of the clusters. Also, they are not very suitable for moving object databases since video data have spatial and temporal characteristics, and high-dimensional attributes. In this paper, we propose a model-based conceptual clustering (MCC) of moving objects in video surveillance based on a formal concept analysis. Our proposed MCC consists of three steps: 'model formation', 'model-based concept analysis', and 'concept graph generation'. The generated concept graph provides conceptual descriptions of moving objects. In order to assess the proposed approach, we conduct comprehensive experiments with artificial and real video surveillance data sets. The experimental results indicate that our MCC dominates two other methods, i.e., generality-based and error-based conceptual clustering algorithms, in terms of quality of concepts.

A study on video data mining

“…The segmented pieces being clustered using the features, an algorithm is used to find whether a segment has normal or abnormal events by clustering and modeling normal events. Lee et al [54] proposed a model-based conceptual clustering of moving objects in video surveillance the basis of on formal concept analysis. The formal concept analysis utilized to generate concepts, handle complicated moving objects and provides conceptual descriptions of moving object databases such as significant features and relationships.…”