Part-Regularized Near-Duplicate Vehicle Re-Identification

He, Bing; Li, Jia; Zhao, Yifan; Tian, Yonghong

doi:10.1109/cvpr.2019.00412

Cited by 268 publications

(195 citation statements)

References 27 publications

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“…Firstly, it can be found that deep learning-based methods (i.e., GoogLeNet [40], NuFACT [41], FACT [3], DRDL [1], VAMI [16], TAMR [22], QD-DLF [24], and PN [17]) are superior to traditional methods (i.e., BOW-CN [38] and LOMO [39]) in the VehicleID database.…”

Section: As Shown Inmentioning

confidence: 99%

Stripe-based and attribute-aware network: a two-branch deep model for vehicle re-identification

Qian

Jiang

Luo

et al. 2020

Meas. Sci. Technol.

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Vehicle re-identification (Re-ID) has been attracting increasing interest in the field of computer vision due to the growing utilization of surveillance cameras in public security. However, vehicle Re-ID still suffers a similarity challenge despite the efforts made to solve this problem. This challenge involves distinguishing different instances with nearly identical appearances. In this paper, we propose a novel two-branch stripebased and attribute-aware deep convolutional neural network (SAN) to learn the efficient feature embedding for vehicle Re-ID task. The two-branch neural network, consisting of stripe-based branch and attribute-aware branches, can adaptively extract the discriminative features from the visual appearance of vehicles. A horizontal average pooling and dimension-reduced convolutional layers are inserted into the stripe-based branch to achieve partlevel features. Meanwhile, the attribute-aware branch extracts the global feature under the supervision of vehicle attribute labels to separate the similar vehicle identities with different attribute annotations. Finally, the part-level and global features are concatenated together to form the final descriptor of the input image for vehicle Re-ID. The final descriptor not only can separate vehicles with different attributes but also distinguish vehicle identities with the same attributes. The extensive experiments on both VehicleID and VeRi databases show that the proposed SAN method outperforms other state-of-the-art vehicle Re-ID approaches.

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Section: As Shown Inmentioning

confidence: 99%

Stripe-based and attribute-aware network: a two-branch deep model for vehicle re-identification

Qian

Jiang

Luo

et al. 2020

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…Bai et al [14] proposed a group sensitive triplet embedding approach that can model the interclass differences. Recently, He et al [20] considered both local and global representations to propose a valid learning framework for vehicle re-ID, however, their method depends on the labeled parts and is therefore labor-intensive. Krizhevsky et al [21] first proposed the use of triplet loss to help the model directly learn feature embedding.…”

Section: Vehicle Re-idmentioning

confidence: 99%

“…To focus on the informative parts, we propose a local feature net that provide supervised attention to the regions of interest, thereby assigns different weights to different parts of the input. Unlike some methods [18][19][20] based on additional information (such as spatial, temporal, and part labels), our method is only based on the images of vehicles.…”

mentioning

confidence: 99%

Local Feature-Aware Siamese Matching Model for Vehicle Re-Identification

et al. 2020

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Vehicle re-identification is attracting an increasing amount of attention in intelligent transportation and is widely used in public security. In comparison to person re-identification, vehicle re-identification is more challenging because vehicles with different IDs are generated by a unified pipeline and cannot only be distinguished based on the subtle differences in their features such as lights, ornaments, and decorations. In this paper, we propose a local feature-aware Siamese matching model for vehicle re-identification. A local feature-aware Siamese matching model focuses on the informative parts in an image and these are the parts most likely to differ among vehicles with different IDs. In addition, we utilize Siamese feature matching to better supervise our attention. Furthermore, a perspective transformer network, which can eliminate image deformation, has been designed for feature extraction. We have conducted extensive experiments on three large-scale vehicle re-ID datasets, i.e., VeRi-776, VehicleID, and PKU-VD, and the results show that our method is superior to the state-of-the-art methods.

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“…With regard to inter-class similarity, the methods of extracting partial discriminative region features [ 3 , 4 ] and generating similar anti-samples [ 5 ] have been put forward. Three even divisions to the global features were used to obtain local features in the Region-Aware deep model (RAM) [ 3 ] model.…”

Section: Introductionmentioning

confidence: 99%

“…Three even divisions to the global features were used to obtain local features in the Region-Aware deep model (RAM) [ 3 ] model. He et al [ 4 ] detected special parts including lights, brands, windows, and jointed these local features with global features to improve the performance. Lou et al [ 5 ] designed a distance adversarial scheme to generate similar hard negative samples, aiming at facilitating the discriminative capability.…”

Section: Introductionmentioning

confidence: 99%

Multi-Proxy Constraint Loss for Vehicle Re-Identification

Chen

Sui

Fang

et al. 2020

Sensors

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Vehicle re-identification plays an important role in cross-camera tracking and vehicle search in surveillance videos. Large variance in the appearance of the same vehicle captured by different cameras and high similarity of different vehicles with the same model poses challenges for vehicle re-identification. Most existing methods use a center proxy to represent a vehicle identity; however, the intra-class variance leads to great difficulty in fitting images of the same identity to one center feature and the images with high similarity belonging to different identities cannot be separated effectively. In this paper, we propose a sampling strategy considering different viewpoints and a multi-proxy constraint loss function which represents a class with multiple proxies to perform different constraints on images of the same vehicle from different viewpoints. Our proposed sampling strategy contributes to better mine samples corresponding to different proxies in a mini-batch using the camera information. The multi-proxy constraint loss function pulls the image towards the furthest proxy of the same class and pushes the image from the nearest proxy of different class further away, resulting in a larger margin between decision boundaries. Extensive experiments on two large-scale vehicle datasets (VeRi and VehicleID) demonstrate that our learned global features using a single-branch network outperforms previous works with more complicated network and those that further re-rank with spatio-temporal information. In addition, our method is easy to plug into other classification methods to improve the performance.

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Part-Regularized Near-Duplicate Vehicle Re-Identification

Cited by 268 publications

References 27 publications

Stripe-based and attribute-aware network: a two-branch deep model for vehicle re-identification

Stripe-based and attribute-aware network: a two-branch deep model for vehicle re-identification

Local Feature-Aware Siamese Matching Model for Vehicle Re-Identification

Multi-Proxy Constraint Loss for Vehicle Re-Identification

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