Abstract:

A trajectory clustering algorithm based on multidimensional Hausdorff distance is proposed for classification of trajectories of moving objects in intelligent visual surveillance system. First, the trajectory of a moving object is described using a sequence of flow vectors. Then the similarity between trajectories is measured by their respective multidimensional Hausdorff distances. Finally, the trajectories are clustered by the spectral clustering algorithm. The proposed algorithm is different from other schemes using Hausdorff distance that it includes both the position and direction information in the flow vectors; hence it can distinguish the trajectories in different directions. A distance preserving transformation is also proposed to reduce the computational complexity of the similarity measure. Experimental results show that, comparing with other algorithm, the clustering accuracy of the proposed algorithm is better, and the proposed distance preserving transformation can greatly reduce the computational cost.

Key words: artificial intelligence, trajectory classification, Hausdorff distance, spectral clustering, distance preserving transformation