Learning Deep Neural Networks for Vehicle Re-ID with Visual-spatio-Temporal Path Proposals
Yantao Shen,Tong Xiao,Hongsheng Li,Shuai Yi,Xiaogang Wang +4 more
- pp 1918-1927
TLDR
In this article, a Siamese-CNN+Path-LSTM model was proposed to incorporate complex spatio-temporal information for regularizing the re-ID results.Abstract:
Vehicle re-identification is an important problem and has many applications in video surveillance and intelligent transportation. It gains increasing attention because of the recent advances of person re-identification techniques. However, unlike person re-identification, the visual differences between pairs of vehicle images are usually subtle and even challenging for humans to distinguish. Incorporating additional spatio-temporal information is vital for solving the challenging re-identification task. Existing vehicle re-identification methods ignored or used oversimplified models for the spatio-temporal relations between vehicle images. In this paper, we propose a two-stage framework that incorporates complex spatio-temporal information for effectively regularizing the re-identification results. Given a pair of vehicle images with their spatiotemporal information, a candidate visual-spatio-temporal path is first generated by a chain MRF model with a deeply learned potential function, where each visual-spatiotemporal state corresponds to an actual vehicle image with its spatio-temporal information. A Siamese-CNN+Path- LSTM model takes the candidate path as well as the pairwise queries to generate their similarity score. Extensive experiments and analysis show the effectiveness of our proposed method and individual components.read more
Citations
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Book ChapterDOI
Person Re-identification with Deep Similarity-Guided Graph Neural Network
TL;DR: Zhang et al. as mentioned in this paper proposed a Similarity-Guided Graph Neural Network (SGGNN) to estimate visual similarities between person images and gallery images in an end-to-end manner.
Proceedings ArticleDOI
Viewpoint-Aware Attentive Multi-view Inference for Vehicle Re-identification
TL;DR: A Viewpoint-aware Attentive Multi-view Inference (VAMI) model that only requires visual information to solve the multi-view vehicle reID problem and achieves consistent improvements over state-of-the-art vehicle re-ID methods on two public datasets: VeRi and VehicleID.
Proceedings ArticleDOI
VERI-Wild: A Large Dataset and a New Method for Vehicle Re-Identification in the Wild
TL;DR: A new method for vehicle ReID is proposed, in which, the ReID model is coupled into a Feature Distance Adversarial Network (FDA-Net), and a novel feature distance adversary scheme is designed to generate hard negative samples in feature space to facilitate Re ID model training.
Proceedings ArticleDOI
Part-Regularized Near-Duplicate Vehicle Re-Identification
TL;DR: This paper proposes a simple but efficient part-regularized discriminative feature preserving method which enhances the perceptive ability of subtle discrepancies in vehicle re-identification and develops a novel framework to integrate part constrains with the global Re-ID modules by introducing an detection branch.
Proceedings ArticleDOI
RAM: A Region-Aware Deep Model for Vehicle Re-Identification
TL;DR: A novel learning algorithm is introduced to jointly use vehicle IDs, types/models, and colors to train the Region-Aware deep Model (RAM), which fuses more cues for training and results in more discriminative global and regional features.
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