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

Jeongkyu Lee; Pragya Rajauria; Subodh Kumar Shah

doi:10.1117/12.708229

29 January 2007 A model-based conceptual clustering of moving objects in video surveillance

Jeongkyu Lee, Pragya Rajauria, Subodh Kumar Shah

Proceedings Volume 6506, Multimedia Content Access: Algorithms and Systems; 650602 (2007) https://doi.org/10.1117/12.708229
Event: Electronic Imaging 2007, 2007, San Jose, CA, United States

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.

Citation Download Citation

Jeongkyu Lee, Pragya Rajauria, and Subodh Kumar Shah "A model-based conceptual clustering of moving objects in video surveillance", Proc. SPIE 6506, Multimedia Content Access: Algorithms and Systems, 650602 (29 January 2007); https://doi.org/10.1117/12.708229

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