Vehicle Re-identification: an Efficient Baseline Using Triplet Embedding

Kuma, Ratnesh; Weill, Edwin; Aghdasi, F.; Sriram, P.

doi:10.1109/ijcnn.2019.8852059

Cited by 95 publications

(62 citation statements)

References 43 publications

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“…For the vehicle ReID problem, the recent work [18] explores the advances in batch-based sampling for triplet embedding that are used for state-of-the-art in person ReID solutions. They compared different sampling variants and demonstrated state-of-the-art results on all vehicle ReID benchmarks [28,26,52], outperforming multi-view-based embedding and most spatio-temporal regularizations (see Tab.…”

Section: Image-based Reidmentioning

confidence: 99%

CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification

Tang

Naphade

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

331

179

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Urban traffic optimization using traffic cameras as sensors is driving the need to advance state-of-the-art multitarget multi-camera (MTMC) tracking. 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. To the best of our knowledge, CityFlow is the largest-scale dataset in terms of spatial coverage and the number of cameras/videos in an urban environment. The dataset contains more than 200K annotated bounding boxes covering a wide range of scenes, viewing angles, vehicle models, and urban traffic flow conditions. Camera geometry and calibration information are provided to aid spatio-temporal analysis. In addition, a subset of the benchmark is made available for the task of image-based vehicle re-identification (ReID). We conducted an extensive experimental evaluation of baselines/state-of-the-art approaches in MTMC tracking, multi-target single-camera (MTSC) tracking, object detection, and image-based ReID on this dataset, analyzing the impact of different network architectures, loss functions, spatio-temporal models and their combinations on task effectiveness. An evaluation server is launched with the release of our benchmark at the 2019 AI City Challenge that allows researchers to compare the performance of their newest techniques. We expect this dataset to catalyze research in this field, propel the state-of-the-art forward, and lead to deployed traffic optimization(s) in the real world.

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Section: Image-based Reidmentioning

confidence: 99%

CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification

Tang

Naphade

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

331

179

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“…The key practices include hard triplet mining, pretraining for identity classification, dataset augmentation with difficult examples, sufficiently large image resolution, and state-of-theart base architecture. Triplet mining and ID classification have been already adopted in vehicle Re-ID [13], [14]. Kumar et al [14] demonstrated an extensive evaluation of contrastive and triplet losses during vehicle Re-ID.…”

Section: A Re-identification (Re-id)mentioning

confidence: 99%

“…Triplet mining and ID classification have been already adopted in vehicle Re-ID [13], [14]. Kumar et al [14] demonstrated an extensive evaluation of contrastive and triplet losses during vehicle Re-ID.…”

Section: A Re-identification (Re-id)mentioning

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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“…Some recent works [17,19,22] combined the classification loss through metric learning. Kumar et al [9] conducted an indepth analysis of the vehicle triplet embeddings, extensively evaluated the loss function, and proved that the use of triplet embeddings is effective.…”

Section: Loss Functions For Embeddingmentioning

confidence: 99%

Background Segmentation for Vehicle Re-identification

Ming-jie

Zhang

et al. 2019

Lecture Notes in Computer Science

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Vehicle re-identification (Re-ID) is very important in intelligent transportation and video surveillance. Prior works focus on extracting discriminative features from visual appearance of vehicles or using visual-spatio-temporal information. However, background interference in vehicle re-identification have not been explored. In the actual large-scale spatio-temporal scenes, the same vehicle usually appears in different backgrounds while different vehicles might appear in the same background, which will seriously affect the re-identification performance. To the best of our knowledge, this paper is the first to consider the background interference problem in vehicle re-identification. We construct a vehicle segmentation dataset and develop a vehicle Re-ID framework with a background interference removal (BIR) mechanism to improve the vehicle Re-ID performance as well as robustness against complex background in large-scale spatio-temporal scenes. Extensive experiments demonstrate the effectiveness of our proposed framework, with an average 9% gain on mAP over state-of-the-art vehicle Re-ID algorithms.

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Vehicle Re-identification: an Efficient Baseline Using Triplet Embedding

Cited by 95 publications

References 43 publications

CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification

CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification

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

Background Segmentation for Vehicle Re-identification

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