A drone-based image processing system for car detection in a smart transport infrastructure

Maria, G.; Baccaglini, Enrico; Brevi, Daniele; Gavelli, Marco; Scopigno, Riccardo

doi:10.1109/melcon.2016.7495454

Cited by 26 publications

(12 citation statements)

References 5 publications

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“…However, the presented systems only detect and recognize the vehicles and verify if they are the registered users or not. Recently, a more interesting work on image processing for smart transport infrastructure is presented in [12] using drones. The presented work uses drone cameras to capture the videos and then analyze it for the available parking lots and congestion on the roads.…”

Section: Related Workmentioning

confidence: 99%

Real-Time Smart Parking Systems Integration in Distributed ITS for Smart Cities

Alam

Moroni

Pieri

et al. 2018

Journal of Advanced Transportation

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Intelligent Transportation Systems (ITS) have evolved as a key research topic in recent years, revolutionizing the overall traffic and travel experience by providing a set of advanced services and applications. These data-driven services contribute to mitigate major problems arising from the ever growing need of transport in our daily lives. Despite the progress, there is still need for an enhanced and distributed solution that can exploit the data from the available systems and provide an appropriate and real-time reaction on transportation systems. Therefore, in this paper, we present a new architecture where the intelligence is distributed and the decisions are decentralized. The proposed architecture is scalable since the incremental addition of new peripheral subsystems is supported by the introduction of gateways which requires no reengineering of the communication infrastructure. The proposed architecture is deployed to tackle the problem of traffic management inefficiency in urban areas, where traffic load is substantially increased, by vehicles moving around unnecessarily, to find a free parking space. This can be significantly reduced through the availability and diffusion of local information regarding vacant parking slots to drivers in a given area. Two types of parking systems, magnetic and vision sensor based, have been introduced, deployed, and tested in different scenarios. The effectiveness of the proposed architecture, together with the proposed algorithms, is assessed in field trials.

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Section: Related Workmentioning

confidence: 99%

Real-Time Smart Parking Systems Integration in Distributed ITS for Smart Cities

Alam

Moroni

Pieri

et al. 2018

Journal of Advanced Transportation

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“…For a Smart Parking scheme to be useful information must not only be secure; it must also be accurate. New technologies are being used increasingly to ensure that information is up-to-date (e.g., Maria et al, 2016). However, unless the existence and merits of such a scheme are effectively publicised, the scheme is likely to be under-used (Peng, Nunes & Zheng, 2017).…”

Section: What Other Factors Influence the Use Of A New Technology?mentioning

confidence: 99%

Investigating the Usage of IoT-Based Smart Parking Services in the Borough of Westminster

Peng

Clough

Madden³

et al. 2021

Journal of Global Information Management

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Smart Parking schemes cannot succeed without the engagement and support of the drivers who may benefit from their use. This study investigates engagement with a Smart Parking service in the London Borough of Westminster. Factors likely to influence the use of Smart Parking services were identified from a literature review and incorporated into an explanatory model comprising 9 factors connected by 16 hypotheses. To test the model, residents of Westminster and visitors to the area were surveyed, resulting in a total of 212 valid responses. The responses were used to test a structural equation model, using confirmatory factor analysis. The results of the analysis indicated that Awareness of the scheme; Perceived Ease of Use; Perceived Usefulness; Cost saving; Perceived Privacy and Perceived Security all had a direct impact on Usage, with Awareness being the most influential factor. The results also highlighted the fact that, despite efforts by Westminster Council to publicise the scheme, 74% of respondents had little awareness of it, suggesting the need for improved publicity.

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“…[26] integrates concepts of R-CNN [18] and YOLO [19] proposing a new model and an inducing layer to optimize the model and speed up convergence. Works such as [27], use a sliding-window-based detector and use a fixed region of interest to remove false positives reducing the necessary computations. However, this still does not solve the problem of having to scale the detection process to larger resolution images.…”

Section: Related Workmentioning

confidence: 99%

Efficient Embedded Deep Neural-Network-based Object Detection Via Joint Quantization and Tiling

Plastiras¹,

Siddiqui²,

Kyrkou³

et al. 2020

2020 2nd IEEE International Conference on Artificial Intelligence Circuits and Systems (AICAS)

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Embedded visual AI is a growing trend in applications requiring low latency, real-time decision support, increased robustness and security. Visual object detection, a key task in visual data analytics, has enjoyed significant improvements in terms of capabilities and accuracy due to the emergence of Convolutional Neural Networks (CNNs). However, such complex paradigms require heavy computational resources that prevent their deployment on resource-constrained devices, and in particular, impose significant constraints in possible hardware accelerators geared towards such applications. In this work therefore, we investigate how a combination of techniques can lead to efficient visual AI pipelines for resource-constrained object detection. In particular we leverage an efficient search strategy based on a combination of pre-processing mechanisms, that reduce the processing demands of deep network as a counter measure for potential accuracy reduction caused by quantization. The proposed approach enables the detection of objects in higher resolution frames using quantized models, while maintaining the accuracy of full-precision CNN-based object detectors. We illustrate the impact on the accuracy and average processing time using quantization techniques and different tiling approaches on efficient object detection architectures; as a case study, we focus on Unmanned-Aerial-Vehicles (UAVs). Through the proposed methodology, hardware accelerator demands are thereby reduced, leading to both performance benefits and associated power savings.

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A drone-based image processing system for car detection in a smart transport infrastructure

Cited by 26 publications

References 5 publications

Real-Time Smart Parking Systems Integration in Distributed ITS for Smart Cities

Real-Time Smart Parking Systems Integration in Distributed ITS for Smart Cities

Investigating the Usage of IoT-Based Smart Parking Services in the Borough of Westminster

Efficient Embedded Deep Neural-Network-based Object Detection Via Joint Quantization and Tiling

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