Proceedings ArticleDOI
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,Hongsheng Li,Xiaogang Wang +9 more
- pp 379-387
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TLDR
Both the orientation invariant feature embedding and the spatio-temporal regularization achieve considerable improvements in the vehicle Re-identification problem.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.read more
Citations
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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.
Posted Content
CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification
Zheng Tang,Milind Naphade,Ming-Yu Liu,Xiaodong Yang,Stan Birchfield,Shuo Wang,Ratnesh Kumar,David C. Anastasiu,Jenq-Neng Hwang +8 more
TL;DR: This work introduces CityFlow, a city-scale traffic camera dataset consisting of more than 3 hours of synchronized HD videos from 40 cameras across 10 intersections, with the longest distance between two simultaneous cameras being 2.5 km.
Proceedings ArticleDOI
A Dual-Path Model With Adaptive Attention for Vehicle Re-Identification
Pirazh Khorramshahi,Amit Kumar,Neehar Peri,Sai Saketh Rambhatla,Jun-Cheng Chen,Rama Chellappa +5 more
TL;DR: In this paper, a dual-path adaptive attention model for vehicle re-identification (AAVER) is proposed, where the global appearance path captures macroscopic vehicle features and the orientation conditioned part appearance path learns to capture localized discriminative features by focusing attention on the most informative key-points.
References
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Proceedings ArticleDOI
Vehicle Re-identification for Automatic Video Traffic Surveillance
Dominik Zapletal,Adam Herout +1 more
TL;DR: The proposed method works with a high accuracy and in 85 milliseconds of the CPU (Core i7) computation time per one vehicle re-identification assuming the fullHD resolution video input.
Journal ArticleDOI
Kernelized Saliency-Based Person Re-Identification Through Multiple Metric Learning
TL;DR: The proposed kernelized saliency-based person re-identification through multiple metric learning has been evaluated on four publicly available benchmark data sets to show its superior performance over the state-of-the-art approaches.