Spatial-temporal Vehicle Re-identification

• URL: http://arxiv.org/abs/2309.01166v1
• Date: Sun, 3 Sep 2023 13:07:38 GMT
• Title: Spatial-temporal Vehicle Re-identification
• Authors: Hye-Geun Kim, YouKyoung Na, Hae-Won Joe, Yong-Hyuk Moon, Yeong-Jun Cho
• Abstract summary: We propose a spatial-temporal vehicle ReID framework that estimates reliable camera network topology. Based on the proposed methods, we performed superior performance on the public dataset (VeRi776) by 99.64% of rank-1 accuracy.
• Score: 3.7748602100709534
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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