Deep joint discriminative learning for vehicle re-identification and retrieval

Li, Yuqi; Li, Yanghao; Yan, Huirong; Liu, Jiaying

doi:10.1109/icip.2017.8296310

Cited by 55 publications

(31 citation statements)

References 5 publications

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“…The performance comparison on VehicleID is summarized in Table 2. We compare our proposed SGAT with 17 state-of-the-art methods on VehicleID including LOMO [15], CCL [16], NuFACT [23], ABLN [51], XVGAN [50], C2F [6], CLVR [10], VAMI [52], RNN-HA [40], SCAN [34], OIFE [39], EALN [25], GSTE [1], RAM [24], QD-DLF [53], DJDL [14], AAVER [11]. On the small test set, SGAT obtains 81.49% mAP score and 78.12% Rank-1 accuracy.…”

Section: Experiments Resultsmentioning

confidence: 99%

A Structured Graph Attention Network for Vehicle Re-Identification

Zhu

Zha

Zhang

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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Vehicle re-identification aims to identify the same vehicle across different surveillance cameras and plays an important role in public security. Existing approaches mainly focus on exploring informative regions or learning an appropriate distance metric. However, they not only neglect the inherent structured relationship between discriminative regions within an image, but also ignore the extrinsic structured relationship among images. The inherent and extrinsic structured relationships are crucial to learning effective vehicle representation. In this paper, we propose a Structured Graph ATtention network (SGAT) to fully exploit these relationships and allow the message propagation to update the features of graph nodes. SGAT creates two graphs for one probe image. One is an inherent structured graph based on the geometric relationship between the landmarks that can use features of their neighbors to enhance themselves. The other is an extrinsic structured graph guided by the attribute similarity to update image representations. Experimental results on two public vehicle re-identification datasets including VeRi-776 and VehicleID have shown that our proposed method achieves significant improvements over the state-of-the-art methods. CCS CONCEPTS • Information systems → Top-k retrieval in databases; • Computing methodologies → Visual content-based indexing and retrieval.

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Section: Experiments Resultsmentioning

confidence: 99%

A Structured Graph Attention Network for Vehicle Re-Identification

Zhu

Zha

Zhang

et al. 2020

Proceedings of the 28th ACM International Conference on Multimedia

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“…They improved the triplet-wise training at two ways: first, a stronger constraint namely classification-oriented loss is augmented with the original triplet loss; second, a new triplet sampling method based on pairwise images is modeled. Li et al (2017b) proposed a deep joint discriminative learning (DJDL) model, which extracts discriminative representations for vehicle images. To exploit properties and relationship among samples in different views, they modeled a unified framework to combine several different tasks efficiently, including identification, attribute recognition, verification and triplet tasks.…”

Section: Deep Feature Based Methodsmentioning

confidence: 99%

“…The 8 handcrafted feature based methods are: the 3d and color information (3DCI)Woesler (2003), the edge-map distances (EMD)Shan et al (2005), the 3d and piecewise model (3DPM)Guo et al (2008), the 3d pose and illumination model (3DPIM)Hou et al (2009), the attribute based model (ABM)Feris et al (2012), the multi-pose model (MPM)Zheng et al (2015), the bounding box model (BBM)Zapletal and (2016), and the license number plate (LNP)Watchar and (2017). The 12 deep feature methods are: the progressive vehicle re-identification (PROVID)Liu et al (2016c), the deep relative distance learning (DRDL)Liu et al (2016a), the deep color and texture (DCT)Liu et al (2016b), the orientation invariant model (OIM)Wang et al (2017), the visual spatio-temporal model (VSTM)Shen et al (2017), the cross-level vehicle recognition (CLVR)Kanacı et al (2017), the triplet-wise training (TWT)Zhang et al (2017), the feature fusing model (FFM)Tang et al (2017), the deep joint discriminative learning (DJDL)Li et al (2017b), the Null space based Fusion of Color and Attribute feature (NuFACT)Liu et al (2018), the multi-view feature (MVF)Zhou et al (2018), and the group sensitive triplet embedding (GSTE)Bai et al (2018).…”

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confidence: 99%

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

Khan

Ullah

2019

Computer Vision and Image Understanding

137

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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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“…Besides, Li et al [73] proposed a deep joint discriminative learning (DJDL) method to train a convolutional neural network which was aimed to extract discriminative feature representations of vehicle images. DJDL incorporated four different subnetworks in a framework, identification and attribute recognition was to exploit specific properties the individual samples, verification task was to constrain relationship between two samples, and triplet task is responsible for constraining the relative distance among three samples.…”

Section: B: Triplet Lossmentioning

confidence: 99%

A Survey of Vehicle Re-Identification Based on Deep Learning

2019

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Vehicle re-identification is one of the core technologies of intelligent transportation systems, and it is crucial for the construction of smart cities. With the rapid development of deep learning, vehicle re-identification technologies have made significant progress in recent years. Therefore, making a comprehensive survey about the vehicle re-identification methods based on deep learning is quite indispensable. There are mainly five types of deep learning-based methods designed for vehicle re-identification, i.e. methods based on local features, methods based on representation learning, methods based on metric learning, methods based on unsupervised learning, and methods based on attention mechanism. The major contributions of our survey come from three aspects. First, we give a comprehensive review of the current five types of deep learning-based methods for vehicle re-identification, and we further compare them from characteristics, advantages, and disadvantages. Second, we sort out vehicle public datasets and compare them from multiple dimensions. Third, we further discuss the challenges and possible research directions of vehicle re-identification in the future based on our survey. INDEX TERMS Deep learning, intelligent transportation system, vehicle re-identification, vehicle public datasets.

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Deep joint discriminative learning for vehicle re-identification and retrieval

Cited by 55 publications

References 5 publications

A Structured Graph Attention Network for Vehicle Re-Identification

A Structured Graph Attention Network for Vehicle Re-Identification

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

A Survey of Vehicle Re-Identification Based on Deep Learning

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