Vehicle Counting and Speed Estimation with RFID Backscatter Signal

Yang, Cheng-Hsun; Tsai, Hsin-Mu

doi:10.1109/vnc48660.2019.9062822

Cited by 6 publications

(2 citation statements)

References 8 publications

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“…Image processing based ANPR integration with RFID technology may help in various road applications and may improve system efficiency as in [ 176 , 177 ]. The integration of RFID and ANPR may result in a hybrid system and it can be considered for multiple applications of intelligent transport systems in present and future [ 176 ].…”

Section: Conclusion and Future Researchmentioning

confidence: 99%

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Lubna

Mufti²,

Shah

2021

Sensors

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Technologies and services towards smart-vehicles and Intelligent-Transportation-Systems (ITS), continues to revolutionize many aspects of human life. This paper presents a detailed survey of current techniques and advancements in Automatic-Number-Plate-Recognition (ANPR) systems, with a comprehensive performance comparison of various real-time tested and simulated algorithms, including those involving computer vision (CV). ANPR technology has the ability to detect and recognize vehicles by their number-plates using recognition techniques. Even with the best algorithms, a successful ANPR system deployment may require additional hardware to maximize its accuracy. The number plate condition, non-standardized formats, complex scenes, camera quality, camera mount position, tolerance to distortion, motion-blur, contrast problems, reflections, processing and memory limitations, environmental conditions, indoor/outdoor or day/night shots, software-tools or other hardware-based constraint may undermine its performance. This inconsistency, challenging environments and other complexities make ANPR an interesting field for researchers. The Internet-of-Things is beginning to shape future of many industries and is paving new ways for ITS. ANPR can be well utilized by integrating with RFID-systems, GPS, Android platforms and other similar technologies. Deep-Learning techniques are widely utilized in CV field for better detection rates. This research aims to advance the state-of-knowledge in ITS (ANPR) built on CV algorithms; by citing relevant prior work, analyzing and presenting a survey of extraction, segmentation and recognition techniques whilst providing guidelines on future trends in this area.

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Section: Conclusion and Future Researchmentioning

confidence: 99%

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Lubna

Mufti²,

Shah

2021

Sensors

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“…Several studies were conducted to estimate vehicle speed and time spent on the road. These studies include [15], [16], [17], [18], [19], [20], [21], [22], [23], and [24]. These studies can be divided into three.…”

Section: Related Workmentioning

confidence: 99%

Feature Engineering Algorithms for Traffic Dataset

Abdullah¹,

Usmani²,

Pillai³

et al. 2021

IJACSA

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As a result of an increase in the human population globally, traffic congestion in the urban area is becoming worse, which leads to time-consuming, waste of fuel, and, most importantly, the emission of pollutants. Therefore, there is a need to monitor and estimate traffic density. The emergence of an automatic traffic management system allows us to record and monitor motor vehicles' movement in a road segment. One of the challenges researchers face is when the historical traffic data is given as an annual average that contains incomplete data. The annual average daily traffic (AADT) is an average number of traffic volumes at the roadway segment in a specific location over a year. An example of AADT data is the one given by Road Traffic Volume Malaysia (RTVM), and this data is incomplete. The RTVM provides an average of daily traffic data and one peak hour. The recorded traffic data is for sixteen hours, and the only hourly data given is one hour, from 8.00 am to 9.00 am. Hence there is a need to estimate hourly traffic volume for the remaining hours. Feature engineering can be used to overcome the issue of incomplete data. This paper proposed feature engineering algorithms that can efficiently estimate hourly traffic volume and generate features from the existing dataset for all traffic census stations in Malaysia using queuing theory. The proposed feature engineering algorithms were able to estimate the hourly traffic volume and generate features for three years in Jalan Kepong census station, Kuala Lumpur, Malaysia. The algorithms were evaluated using the Random Forest model and Decision Tree Models. The result shows that our feature engineering algorithms improve machine learning algorithms' performance except for the prediction of N O2 using Random Forest, which shows the highest MAE, MSE, and RMSE when traffic data was included for prediction. The algorithm is applied in one of the traffic census stations in Kuala Lumpur, and it can be used for the other stations in Malaysia. Additionally, the algorithm can also be used for any annual average daily traffic data if it includes average hourly data.

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A Framework to Determine Secure Distances for Either Drones or Robots Based Inventory Management Systems

et al. 2020

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We propose a framework to determine a secure distance between a drone with an ultrahigh-frequency band radio frequency identification (UHF band RFID) reader and metallic objects affixed with RFID tags. The secure distance avoids order changes in received signal strength indicator (RSSI) values among the identified RFID tags in the field of view of the RFID reader. This distance enables a drone operator to securely operate the drone while identifying the RFID tag on the front of an object based on the measurements of RSSI values. An RFID tag located on the front of an object provides the maximum RSSI value. However, multipath propagation alters the RSSI values. Therefore, a framework is needed to determine a secure distance considering the multipath effects. Although inventory management systems based on drones and RFID systems have been proposed to date, this article establishes a framework to determine the secure distance. In the proposed framework, RFID tag and reader radiation patterns and multipath propagation effects were considered. The proposed framework was evaluated theoretically and experimentally. To evaluate and demonstrate the secure distance, we measured the RSSI values of two RFID tags attached to a metallic balcony. The height from the ground and spacing of the two RFID tags were 1.5 m and 1.3 m, respectively. In this environment, the secure distance was 3.8 m. The experimentally obtained distance that avoids order changes in RSSI values corresponded well with that obtained by this framework. The proposed secure distance is crucial when either drones or robots are introduced to inventory management systems.

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Vehicle Counting and Speed Estimation with RFID Backscatter Signal

Cited by 6 publications

References 8 publications

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Automatic Number Plate Recognition:A Detailed Survey of Relevant Algorithms

Feature Engineering Algorithms for Traffic Dataset

A Framework to Determine Secure Distances for Either Drones or Robots Based Inventory Management Systems

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