Spatial-temporal Vehicle Re-identification
Hye-Geun Kim YouKyoung Na Hae-Won Joe Yong-Hyuk Moon Yeong-Jun Cho
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.