BoxCars: 3D Boxes as CNN Input for Improved Fine-Grained Vehicle Recognition

Sochor, Jakub; Herout, Adam; Havel, Jiří

doi:10.1109/cvpr.2016.328

Cited by 149 publications

(105 citation statements)

References 35 publications

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“…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%

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)

177

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%

“…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. Wang et al [31] learn a feature for each side of the vehicle and then credit the feature of visible side during matching.…”

Section: Related Workmentioning

confidence: 99%

Vehicle Re-Identification With Viewpoint-Aware Metric Learning

Chu

Sun

Li³

et al. 2019

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

177

114

View full text Add to dashboard Cite

show abstract

“…As opposed to our approach, OIFE+ requires keypoint annotations and a separate metric learning module from [54]. Furthermore OIFE combines VeRi, VehicleID, CompCars [29] and Cars21k [42] into one large train set.…”

Section: Vehicleidmentioning

confidence: 99%

Vehicle Re-identification: an Efficient Baseline Using Triplet Embedding

Kuma

Weill

Aghdasi

et al. 2019

2019 International Joint Conference on Neural Networks (IJCNN)

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1 In this paper we tackle the problem of vehicle reidentification in a camera network utilizing triplet embeddings. Re-identification is the problem of matching appearances of objects across different cameras. With the proliferation of surveillance cameras enabling smart and safer cities, there is an ever-increasing need to re-identify vehicles across cameras. Typical challenges arising in smart city scenarios include variations of viewpoints, illumination and self occlusions. Most successful approaches for reidentification involve (deep) learning an embedding space such that the vehicles of same identities are projected closer to one another, compared to the vehicles representing different identities. Popular loss functions for learning an embedding (space) include contrastive or triplet loss. In this paper we provide an extensive evaluation of these losses applied to vehicle re-identification and demonstrate that using the best practices for learning embeddings outperform most of the previous approaches proposed in the vehicle reidentification literature. Compared to most existing stateof-the-art approaches, our approach is simpler and more straightforward for training utilizing only identity-level annotations, along with one of the smallest published embedding dimensions for efficient inference. Furthermore in this work we introduce a formal evaluation of a triplet sampling variant (batch sample) into the re-identification literature.

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“…Sochor et al (2016) collected BoxCars dataset from 137 surveillance cameras. This dataset consists of two variants: BoxCars21K and BoxCars116K.…”

mentioning

confidence: 99%

A survey of advances in vision-based vehicle re-identification

Khan

Ullah

2019

Computer Vision and Image Understanding

129

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Vehicle re-identification (V-reID) has become significantly popular in the community due to its applications and research significance. In particular, the V-reID is an important problem that still faces numerous open challenges. This paper reviews different V-reID methods including sensor based methods, hybrid methods, and vision based methods which are further categorized into hand-crafted feature based methods and deep feature based methods. The vision based methods make the V-reID problem particularly interesting, and our review systematically addresses and evaluates these methods for the first time. We conduct experiments on four comprehensive benchmark datasets and compare the performances of recent hand-crafted feature based methods and deep feature based methods. We present the detail analysis of these methods in terms of mean average precision (mAP) and cumulative matching curve (CMC). These analyses provide objective insight into the strengths and weaknesses of these methods. We also provide the details of different V-reID datasets and critically discuss the challenges and future trends of V-reID methods.

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BoxCars: 3D Boxes as CNN Input for Improved Fine-Grained Vehicle Recognition

Cited by 149 publications

References 35 publications

Vehicle Re-Identification With Viewpoint-Aware Metric Learning

Vehicle Re-Identification With Viewpoint-Aware Metric Learning

Vehicle Re-identification: an Efficient Baseline Using Triplet Embedding

A survey of advances in vision-based vehicle re-identification

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