Noise-Tolerant Trajectory Distance Computation in the Presence of Inherent Noise for Video Surveillance Applications

As the importance of trajectory analysis arises in video surveillance, it becomes crucial to define the dissimilarity measure between two trajectories. Although the Hausdorff distance can be considered as a viable candidate for the measure, it is challenging to deal with noise present in trajectories since the Hausdorff distance is susceptible to noise so that even a single noise point may significantly distort the distance computation. In this paper, we propose a novel approach to alleviate the influence of inherent noise by setting noise-like points apart from ordinary points with a novel spatial tree structure during trajectory distance computation, without additional noise detection processes. In particular, we present
$\mathrm {R}^{on}$
-tree, an extension of the existing spatial tree structure, that seamlessly finds permanent noise-like points, which are considered to have a low possibility of being ordinary points, and then keeps them in a separate auxiliary R-tree, without any separate process of disclosing noise-like points. We exploit
$\mathrm {R}^{on}$
-tree to compute the noise-tolerant trajectory distance by modifying an existing algorithm for the Hausdorff distance. We also build an algorithm for noise-tolerant trajectory search to ensure accurate and high-quality search results even with noisy trajectories. The empirical results show that in all cases, our proposed approach yields the distance closest to the true one than any other competitor. The effectiveness of our approach is further examined by applying our noise-tolerant trajectory search to a real video surveillance dataset.