Vehicle re-identification using wireless magnetic sensors: Algorithm revision, modifications and performance analysis

Sanchez, Rene O.; Flores, Christopher; Horowitz, Roberto; Rajagopal, Ram; Varaiya, Pravin

doi:10.1109/icves.2011.5983819

Cited by 17 publications

(7 citation statements)

References 5 publications

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“…As a result, from Eqn. (18), we conclude that {z j (t)} T t=1 ∈ P o −j . We define the circumradius of a convex subset P of the finite dimension Euclidean space as the radius of the smallest Euclidean ball containing P and denote it by R(P ).…”

Section: Appendix a Proof Of Theoremmentioning

confidence: 68%

“…(19) for k ∈ S j are satisfied by the definition in Eqn. (18). Also, note that for k / ∈ S j , we have w k,j (t) = 0.…”

Section: Appendix a Proof Of Theoremmentioning

confidence: 99%

“…Additionally, attention to privacy issues has raised more concerns over image acquisition. In contrast, further alternatives such as magnetic sensors and microphones are inexpensive and obviate privacy concerns [18]. Vehicles are primarily of metallic structure that perturb the earth's magnetic field, and hence produce unique magnetic signatures that have served to discriminate between vehicles [19] [20].…”

mentioning

confidence: 99%

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Robust Group Subspace Recovery: A New Approach for Multi-Modality Data Fusion

Ghanem,

Panahi,

Krim

et al. 2020

Preprint

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Robust Subspace Recovery (RoSuRe) algorithm was recently introduced as a principled and numerically efficient algorithm that unfolds underlying Unions of Subspaces (UoS) structure, present in the data. The union of Subspaces (UoS) is capable of identifying more complex trends in data sets than simple linear models. We build on and extend RoSuRe to prospect the structure of different data modalities individually. We propose a novel multi-modal data fusion approach based on group sparsity which we refer to as Robust Group Subspace Recovery (RoGSuRe). Relying on a bi-sparsity pursuit paradigm and non-smooth optimization techniques, the introduced framework learns a new joint representation of the time series from different data modalities, respecting an underlying UoS model. We subsequently integrate the obtained structures to form a unified subspace structure. The proposed approach exploits the structural dependencies between the different modalities data to cluster the associated target objects. The resulting fusion of the unlabeled sensors' data from experiments on audio and magnetic data has shown that our method is competitive with other state of the art subspace clustering methods. The resulting UoS structure is employed to classify newly observed data points, highlighting the abstraction capacity of the proposed method.

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Section: Appendix a Proof Of Theoremmentioning

confidence: 68%

“…(19) for k ∈ S j are satisfied by the definition in Eqn. (18). Also, note that for k / ∈ S j , we have w k,j (t) = 0.…”

Section: Appendix a Proof Of Theoremmentioning

confidence: 99%

mentioning

confidence: 99%

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Robust Group Subspace Recovery: A New Approach for Multi-Modality Data Fusion

Ghanem,

Panahi,

Krim

et al. 2020

Preprint

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“…As observed by Tian et al [14], this problem is still an open issue for future developments of networked video surveillance systems, in which the road camera infrastructure is used to extract vehicle trajectories for behavior analysis and pattern discovery. Traditionally, algorithms proposed for this task were based on the comparison of electromagnetic signatures captured from a pair of inductive or magnetic sensors [15], [16]. This class of systems can benefit from the existing infrastructure to capture vehicle signature profiles from inductive-loop detectors [3], weight-in-motion devices [17], and microloop sensors [4].…”

Section: Related Workmentioning

confidence: 99%

Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks

et al. 2021

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This work addresses the problem of vehicle identification through non-overlapping cameras. As our main contribution, we introduce a novel dataset for vehicle identification, called Vehicle-Rear, that contains more than three hours of high-resolution videos, with accurate information about the make, model, color and year of nearly 3,000 vehicles, in addition to the position and identification of their license plates. To explore our dataset we design a two-stream Convolutional Neural Network (CNN) that simultaneously uses two of the most distinctive and persistent features available: the vehicle's appearance and its license plate. This is an attempt to tackle a major problem: false alarms caused by vehicles with similar designs or by very close license plate identifiers. In the first network stream, shape similarities are identified by a Siamese CNN that uses a pair of low-resolution vehicle patches recorded by two different cameras. In the second stream, we use a CNN for Optical Character Recognition (OCR) to extract textual information, confidence scores, and string similarities from a pair of high-resolution license plate patches. Then, features from both streams are merged by a sequence of fully connected layers for decision. In our experiments, we compared the twostream network against several well-known CNN architectures using single or multiple vehicle features. The architectures, trained models, and dataset are publicly available at https:// github.com/ icarofua/ vehicle-rear.INDEX TERMS Vehicle identification, vehicle matching, multi-stream neural networks, feature fusion.

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“…This temporal data can be used in travel time estimation and V-reID process. Sanchez et al (2011) proposed a wireless magnetic sensor to detect the vehicle signature. Charbonnier et al (2012) proposed a single three-axis magnetic sensor to detect a tri-dimensional magnetic signature.…”

Section: Magnetic Sensorsmentioning

confidence: 99%

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

Khan

Ullah

2019

Computer Vision and Image Understanding

133

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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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Vehicle re-identification using wireless magnetic sensors: Algorithm revision, modifications and performance analysis

Cited by 17 publications

References 5 publications

Robust Group Subspace Recovery: A New Approach for Multi-Modality Data Fusion

Robust Group Subspace Recovery: A New Approach for Multi-Modality Data Fusion

Vehicle-Rear: A New Dataset to Explore Feature Fusion for Vehicle Identification Using Convolutional Neural Networks

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

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