Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-temporal Path Proposals 学习笔记

最新推荐文章于 2025-07-27 13:15:30 发布

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最新推荐文章于 2025-07-27 13:15:30 发布 · 1.1k 阅读

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该论文提出一种两阶段框架用于车辆重识别，结合复杂的时空信息有效规范识别结果。通过链MRF建模生成视觉时空路径提案，并使用深度神经网络学习成对的视觉时空潜在函数。Siamese-CNN+Path-LSTM网络用于确定查询图像对的车辆身份匹配度。

Abstract and Introduce

In this paper, we propose a two-stage framework that incorporates complex spatio-temporal information for effectively regularizing the re-identification results.
If a vehicle is observed at both camera A and C, the same vehicle has to appear at camera B as well. Therefore, given a pair of vehicle images at location A and C, if an image with similar appearance is never observed at camera B at a proper time, their matching confidence should be very low.

The main contribution of our method is two-fold. (1) We propose a two-stage framework for vehicle re-identification. It first proposes a series of candidate visual-spatio-temporal