Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-identification

Wang, Zhongdao; Tang, Luming; Liu, Xihui; Yao, Zhuliang; Yi, Shuai; Shao, Jing; Yan, Junjie; Wang, Shengjin; Li, Hongsheng; Wang, Xiaogang

doi:10.1109/iccv.2017.49

Cited by 336 publications

(316 citation statements)

References 21 publications

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“…Due to the wide application in urban surveillance and intelligent transportation, vehicle re-ID gains rapidly increasing attention in the past two years. To enhance re-ID capacity, some approaches utilize extra attribute information e.g., model/type, color to guide visionbased representation learning [5,15,17,31,26,38,40,39,18]. For instance, Liu et al [13] introduce a two-branch retrieval pipeline to extract both model and instance differences.…”

Section: Related Workmentioning

confidence: 99%

“…Yan et al [32] explore multi-grain relationships of vehicles with multi-level attributes. Other works investigate spatial-temporal association, which gains extra benefit from the topology information of cameras [31,26,16]. In comparison with the above-mentioned works, our method only relies on ID supervision and auxiliary viewpoint information, which is relatively resource-efficient, and yet achieves competitive re-ID accuracy.…”

Section: Related Workmentioning

confidence: 99%

“…viewpoint variation issue. Some works inject orientation information into feature embedding explicitly [27] or implicitly [31]. Sochor et al [27] embed the orientation vector into feature map and gain viewpoint awareness.…”

Section: Related Workmentioning

confidence: 99%

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Vehicle Re-Identification With Viewpoint-Aware Metric Learning

Chu

Sun

Li³

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

185

114

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This paper considers vehicle re-identification (re-ID) problem. The extreme viewpoint variation (up to 180 degrees) poses great challenges for existing approaches. Inspired by the behavior in human's recognition process, we propose a novel viewpoint-aware metric learning approach. It learns two metrics for similar viewpoints and different viewpoints in two feature spaces, respectively, giving rise to viewpoint-aware network (VANet). During training, two types of constraints are applied jointly. During inference, viewpoint is firstly estimated and the corresponding metric is used. Experimental results confirm that VANet significantly improves re-ID accuracy, especially when the pair is observed from different viewpoints. Our method establishes the new state-of-the-art on two benchmarks. 12 * Corresponding author 1 R. Chu and Y. Sun share equal contribution. 2 Work done at Megvii Technology.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Vehicle Re-Identification With Viewpoint-Aware Metric Learning

Chu

Sun

Li³

et al. 2019

2019 IEEE/CVF International Conference on Computer Vision (ICCV)

185

114

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“…Thus, a model must be able to capture the full range of feature combinations in vehicles across multiple brands, orientations, colors, resolutions, and scales, as have been proposed in existing approaches. Consequently, existing approaches build complex networks that perform inter-class similarity discrimination, and intra-class variability minimization in the same model: OIFE [23] uses 20 stacked convolutional networks to extract human-labeled keypoints; RAM [16] builds three sub-networks to evaluate each section of a vehicle (roof, body, chassis); VAMI [19] creates multiple sub-models for each orientation; and QD-DLF [20] builds four networks to extract diagonal features.…”

Section: B Teamed Classifiers For Vehicle Re-idmentioning

confidence: 99%

“…We can build smaller models compared to recent approaches. As such, each member of the intra-class variability team uses a single ResNet 18 backbone and can operate in near real-time, compared to 20 stacked convolutional networks in [23], 5 ResNet backbones in [16], and 4 ResNet50 backbones in [20].…”

Section: B Teamed Classifiers For Vehicle Re-idmentioning

confidence: 99%

Robust, Extensible, and Fast: Teamed Classifiers for Vehicle Tracking in Multi-Camera Networks

Suprem

Lima

Padilha

et al. 2019

2019 IEEE First International Conference on Cognitive Machine Intelligence (CogMI)

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As camera networks have become more ubiquitous over the past decade, the research interest in video management has shifted to analytics on multi-camera networks. This includes performing tasks such as object detection, attribute identification, and vehicle/person tracking across different cameras without overlap. Current frameworks for management are designed for multi-camera networks in a closed dataset environment where there is limited variability in cameras and characteristics of the surveillance environment are well known. Furthermore, current frameworks are designed for offline analytics with guidance from human operators for forensic applications. This paper presents a teamed classifier framework for video analytics in heterogeneous many-camera networks with adversarial conditions such as multi-scale, multi-resolution cameras capturing the environment with varying occlusion, blur, and orientations. We describe an implementation for vehicle tracking and vehicle re-identification (re-id), where we implement a zero-shot learning (ZSL) system that performs automated tracking of all vehicles all the time. Our evaluations on VeRi-776 and Cars196 show the teamed classifier framework is robust to adversarial conditions, extensible to changing video characteristics such as new vehicle types/brands and new cameras, and offers real-time performance compared to current offline video analytics approaches.

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Efficient virtual data search for annotation‐free vehicle reidentification

Wan

Wang

et al. 2022

Int J of Intelligent Sys

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Vehicle reidentification (re‐ID) is the task of retrieving the same vehicle across nonoverlapping cameras, which has made significant progress with the help of abundant manually annotated real images. To avoid the time‐consuming and tedious labeling of real images, virtual data sets with large‐scale synthetic images have recently been constructed to perform annotation‐free model training. However, current methods fail to exploit the potential of virtual data search, that is, searching valuable and representative virtual subdata set for efficient training. This paper presents a novel data sampling strategy from both semantic and feature levels to perform an effective data search. The semantic level determines the sample number of each vehicle identity via the consistency constraint of attribute distribution for source domain and target domain; while the feature level searches valuable and representative samples of each vehicle identity. To our knowledge, we are among the first attempts to search effective virtual data to perform annotation‐free vehicle re‐ID. Extensive cross‐domain experiments from virtual vehicle re‐ID data sets to real vehicle re‐ID data sets show that our data sampling strategy can significantly reduce the training data volume and even boost the re‐ID performance.

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Orientation Invariant Feature Embedding and Spatial Temporal Regularization for Vehicle Re-identification

Cited by 336 publications

References 21 publications

Vehicle Re-Identification With Viewpoint-Aware Metric Learning

Vehicle Re-Identification With Viewpoint-Aware Metric Learning

Robust, Extensible, and Fast: Teamed Classifiers for Vehicle Tracking in Multi-Camera Networks

Efficient virtual data search for annotation‐free vehicle reidentification

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