Spatial-temporal Vehicle Re-identification
This addresses the problem of appearance ambiguities in vehicle tracking for public safety and traffic control, representing a strong specific gain.
The paper tackled vehicle re-identification across cameras by proposing a spatial-temporal framework that combines appearance and spatial-temporal similarities, achieving 99.64% rank-1 accuracy on the VeRi776 dataset.
Vehicle re-identification (ReID) in a large-scale camera network is important in public safety, traffic control, and security. However, due to the appearance ambiguities of vehicle, the previous appearance-based ReID methods often fail to track vehicle across multiple cameras. To overcome the challenge, we propose a spatial-temporal vehicle ReID framework that estimates reliable camera network topology based on the adaptive Parzen window method and optimally combines the appearance and spatial-temporal similarities through the fusion network. Based on the proposed methods, we performed superior performance on the public dataset (VeRi776) by 99.64% of rank-1 accuracy. The experimental results support that utilizing spatial and temporal information for ReID can leverage the accuracy of appearance-based methods and effectively deal with appearance ambiguities.