ID Repair for Trajectories with Transition Graphs

In many surveillance applications, capture devices are set on fixed locations to track entities, leading to valuable spatio-temporal trajectories. However, sometimes the IDs of the entities in these trajectories are incorrectly identified due to various reasons (e.g., illumination conditions and partial occlusion). Since very often the movements of the entities are constrained by certain restrictions imposed by the application (e.g., vehicles must move along the given road network), we consider how to repair the erroneous IDs using transition graphs derived from such restrictions. Roughly speaking, the occurrence of erroneous IDs can cause a valid trajectory to be broken into trajectory fragments that violate some movement constraints imposed by the transition graph, and we aim to repair them by rewriting the IDs and merging the fragments. This problem is practically challenging since it is not easy to judge which IDs in the dataset are correct, and also there may be multiple candidates as the correct value for a single error. We formulate the repair process as an optimization problem and propose a two-phase repair paradigm, which includes candidate repair generation and compatible repair selection, to maximize the quality improvement estimated by a designed objective function. Though both phases are intractable, we propose effective algorithms to solve them through exploiting the locality and sparsity of trajectories. We further devise an index structure, as well as a pruning method to make the repair process more efficient. Experiments on both real and synthetic datasets demonstrate the effectiveness and efficiency of the proposed methods.