Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-Identification
Zhongdao Wang, Luming Tang, Xihui Liu, Zhuliang Yao, Shuai Yi, Jing Shao, Junjie Yan, Shengjin Wang
Abstract
In this paper, we tackle the vehicle Re-identification (ReID) problem which is of great importance in urban surveillance and can be used for multiple applications. In our vehicle ReID framework, an orientation invariant feature embedding module and a spatial-temporal regularization module are proposed. With orientation invariant feature embedding, local region features of different orientations can be extracted based on 20 key point locations and can be well aligned and combined. With spatial-temporal regularization, the log-normal distribution is adopted to model the spatial-temporal constraints and the retrieval results can be refined. Experiments are conducted on public vehicle ReID datasets and our proposed method achieves state-of-the-art performance. Investigations of the proposed framework is conducted, including the landmark regressor and comparisons with attention mechanism. Both the orientation invariant feature embedding and the spatio-temporal regularization achieve considerable improvements.
BibTeX
@inproceedings{iccv2017_orientationinvar,
title = {Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-Identification},
author = {Zhongdao Wang and Luming Tang and Xihui Liu and Zhuliang Yao and Shuai Yi and Jing Shao and Junjie Yan and Shengjin Wang and Hongsheng Li and Xiaogang Wang},
booktitle = {ICCV 2017},
year = {2017}
}