RFTraffic: a study of passive traffic awareness using emitted RF noise from the vehicles

Ding, Yong; Banitalebi, Behnam; Miyaki, Takashi; Beigl, Michael

doi:10.1186/1687-1499-2012-8

Cited by 11 publications

(3 citation statements)

References 41 publications

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“…Vehicle Recognition. As part of the vehicle recognition [37], this phase plays an important role. Furthermore, included is a machine learning algorithm called delta rule (Widrow-Hoff learning rule).…”

Section: Ioopl Implementationmentioning

confidence: 99%

A Machine Learning Algorithm to Automate Vehicle Classification and License Plate Detection

Srividhya

Kavitha

Lai

et al. 2022

Wireless Communications and Mobile Computing

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In the field of intelligent transportation systems (ITS), video surveillance is a hot research topic; this surveillance is used in a variety of applications, such as detecting the cause of an accident, tracking down a specific vehicle, and discovering routes between major locations. Object detection and shadow elimination are the main tasks in this area. Object detection in computer vision is a critical and vital part of object and scene recognition, and its applications are vast in the fields of surveillance and artificial intelligence. Additionally, other challenges arise in regard to video surveillance, including the recognition of text. Based on shadow elevation, we present in this work an inner-outer outline profile (IOOPL) algorithm for detecting the three levels of object boundaries. A system of video surveillance monitoring of traffic can be incorporated into this method. It is essential to identify the type of detected objects in intelligent transportation systems (ITS) to track safely and estimate traffic parameters correctly. This work addresses the problem of not recognizing object shadows as part of the object itself in-vehicle image segmentation. This paper proposes an approach for detecting and segmenting vehicles by eliminating their shadow counterparts using the delta learning algorithm (Widrow-Hoff learning rule), where the system is trained with various types of vehicles according to their appearance, colors, and build types. An essential aspect of the intelligent transportation system is recognizing the type of the detected object so that it can be tracked reliably and the traffic parameters can be estimated correctly. Furthermore, we propose to classify vehicles using a machine learning algorithm consisting of artificial neural networks trained using the delta learning algorithm, a high-performance machine learning algorithm, to obtain information regarding their travels. The paper also presents a method for recognizing the number plate using text correlation and edge dilation techniques. In regard to video text recognition, number plate recognition is a challenging task.

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Section: Ioopl Implementationmentioning

confidence: 99%

A Machine Learning Algorithm to Automate Vehicle Classification and License Plate Detection

Srividhya

Kavitha

Lai

et al. 2022

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

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“…Kassem et al [156] sense traffic situations by tracking frequency and speed of passing cars that intercept the direct line of sight between a pair of nodes on both sides of the road. Furthermore, Ding et al demonstrated, how emissions from car engines can be utilised for passive traffic awareness utilising either roadside installations or also incar modules [81], [157]. The authors have employed standard machine learning approaches in order to distinguish six traffic situations from roadside measurements and, in addition, the own-vehicule's speed with in-car measurements.…”

Section: A Environmental Conditionsmentioning

confidence: 99%

Recent Advances and Challenges in Ubiquitous Sensing

Sigg,

Kunze,

2015

Preprint

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Ubiquitous sensing is tightly coupled with activity recognition. This survey reviews recent advances in Ubiquitous sensing and looks ahead on promising future directions. In particular, Ubiquitous sensing crosses new barriers giving us new ways to interact with the environment or to inspect our psyche. Through sensing paradigms that parasitically utilise stimuli from the noise of environmental, third-party pre-installed systems, sensing leaves the boundaries of the personal domain. Compared to previous environmental sensing approaches, these new systems mitigate high installation and placement cost by providing a robustness towards process noise. On the other hand, sensing focuses inward and attempts to capture mental activities such as cognitive load, fatigue or emotion through advances in, for instance, eye-gaze sensing systems or interpretation of body gesture or pose. This survey summarises these developments and discusses current research questions and promising future directions.

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“…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]. Audio sensors have also been extensively employed in the area of vehicle classification for different applications, and have proven their effectiveness and robustness [21] [22].…”

mentioning

confidence: 99%

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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RFTraffic: a study of passive traffic awareness using emitted RF noise from the vehicles

Cited by 11 publications

References 41 publications

A Machine Learning Algorithm to Automate Vehicle Classification and License Plate Detection

A Machine Learning Algorithm to Automate Vehicle Classification and License Plate Detection

Recent Advances and Challenges in Ubiquitous Sensing

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

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