A Deep Learning-Based Approach to Progressive Vehicle Re-identification for Urban Surveillance

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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“…3. For example, p 1 is equal to the average of f 1,1 , f 1,2 · · · f 1,15 , f 1,16 , f 2,1 , f 2,2 · · · f 2,15 , f 2,16 , that is,…”

Section: A Sanunclassified

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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“…The naturally induced sparsity of the re-id task lies in high inter-class similarity, since we observe that the interclass similarity problem is precisely due to the underlying manufacturing process; some examples of inter-class similarity clusters include groups of Toyota Corollas, black SUVs, or red vehicles. Conversely, existing vehicle re-id datasets such as VeRi-776 [35] and VeRi-Wild [36] primarily focus on intraclass variability. Current approaches in vehicle re-id attempt to address inter-class similarity and intra-class variability in the same end-to-end model [16], [17], [18], [20].…”

Section: B Research Issues In Teamed Classifiersmentioning

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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“…The PROVID [17] also uses the vehicle features and license plate features, but the vehicle features are extracted by conventional methods. Besides, the PROVID is a layered structure.…”

Section: Deep Relative Distance Learningmentioning

confidence: 99%

Computer Science & Information Technology (CS & IT)

2017

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A Deep Learning-Based Approach to Progressive Vehicle Re-identification for Urban Surveillance

Cited by 486 publications

References 22 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

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

Computer Science & Information Technology (CS & IT)

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