Spatial clustering is one of the vital research directions in spatial data mining and knowledge discovery. However, constrained by the complex distribution of uneven density, various shapes and multi-bridge connection of points, most clustering algorithms based on distance or density cannot identify high aggregative point sets efficiently and effectively. A point clustering method based on spatial proximity was proposed. According to the structure of point Voronoi diagram, adjacent relationships among points were recognized. The similarity criteria was defined by region of Voronoi, a tree structure was built to recognize point-target clusters. The comparison experiments were conducted on the proposed algorithm, K-means algorithm and Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm. Results show that the proposed algorithm is capable for identifying clusters in arbitrary shapes, with different densities and connected only at bridges or chains, meanwhile also suitable for aggregative pattern recognition in heterogeneous space.

Accurate background model is the paramount base for object extracting and tracing. In response to swing objects which part quasi-periodically changed in intricate scene, based on multi-Gaussian background model, a new Quasi-Periodic Background Algorithm (QPBA) was proposed to suppress the swing objects and establish an accurate and stable background model. The specific process included: According to multi-Gaussian background model, the object classification in scene was set up, and the effect on Gaussian model's parameters caused by swing objects was analyzed. By using color distribution values as samples to establish Gaussian model to keep swing pixels, the swing model in swing pixels was integrated into background model with weight factors of occurrence frequency and time interval. Comparison among QPBA and the classical background modeling algorithms such as GMM (Gaussian Mixture Model), ViBe (Visual Background extractor) and CodeBook was put forward, and the results were assessed in aspects of quality, quantity and efficiency. It shows that QPBA has a more obvious suppression on swing objects, and its fall-out ratio is less than 1%, so that it can handle the scene with swing objects. At the same time, its correct detection number is consistent with other algorithms, thus the moving objects can be reserved perfectly. In addition, the efficiency of QPBA is high, and its resolving time is approximate to CodeBook, which can satisfy the requirements of real-time computation.

The hierarchical Voronoi diagrams were built through an adaptive clustering method of spatial point clusters. Based on the hierarchical Voronoi diagrams, the topology, density and scope similarities were calculated. The radian and distance similarity were calculated in combination of the standard deviation in mathematical statistics. On the base of every dimensional similarity, the principle of point cluster similarity was decided by the geometrical mean of these parameters. This optimizes the method of the point cluster similarity and the experiment proves its feasibility.