Intelligent drone-based surveillance: application to parking lot monitoring and detection

Sarkar, Sayani; Totaro, Michael W.; Elgazzar, Khalid

doi:10.1117/12.2518320

Cited by 26 publications

(11 citation statements)

References 10 publications

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“…From the sensing functionality perspective, recent work in the area of parking occupancy detection can be divided into three categories: wireless sensor network (WSN) solution [12], [14], [34], [26]- [33], moving sensor solution [35], [36], [45], [37]- [44], and vision-based solution [12], [13], [22]- [25], [14]- [21].…”

Section: Literature Reviewmentioning

confidence: 99%

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A Smart, Efficient, and Reliable Parking Surveillance System With Edge Artificial Intelligence on IoT Devices

Zhuang

et al. 2021

IEEE Trans. Intell. Transport. Syst.

144

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Cloud computing has been a main-stream computing service for years. Recently, with the rapid development in urbanization, massive video surveillance data are produced at an unprecedented speed. A traditional solution to deal with the big data would require a large amount of computing and storage resources. With the advances in Internet of things (IoT), artificial intelligence, and communication technologies, edge computing offers a new solution to the problem by processing all or part of the data locally at the edge of a surveillance system.In this study, we investigate the feasibility of using edge computing for smart parking surveillance tasks, specifically, parking occupancy detection using the real-time video feed. The system processing pipeline is carefully designed with the consideration of flexibility, online surveillance, data transmission, detection accuracy, and system reliability. It enables artificial intelligence at the edge by implementing an enhanced single shot multibox detector (SSD). A few more algorithms are developed either locally at the edge of the system or on the centralized data server targeting optimal system efficiency and accuracy. Thorough field tests were conducted in the Angle Lake parking garage for three months. The experimental results are promising that the final detection method achieves over 95% accuracy in real-world scenarios with high efficiency and reliability. The proposed smart parking surveillance system is a critical component of smart cities and can be a solid foundation for future applications in intelligent transportation systems.

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Section: Literature Reviewmentioning

confidence: 99%

“…We summarize the second category as using moving sensors for parking occupancy detection [35], [36], [45], [37]- [44]. This group of work usually uses sensors on phone apps or probe vehicles to monitor urban parking availability via crowdsensing strategies.…”

Section: Literature Reviewmentioning

confidence: 99%

A Smart, Efficient, and Reliable Parking Surveillance System With Edge Artificial Intelligence on IoT Devices

Zhuang

et al. 2021

IEEE Trans. Intell. Transport. Syst.

144

View full text Add to dashboard Cite

show abstract

“…Sarkar et al [13] proposed a UAV-assisted quick and efficient monitoring solution for real-time parking occupancy and license plate detection.…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Based Parking Vacancy Detection for Smart Cities

Xu¹,

Sun²,

Fu³

et al. 2022

Proceedings of the Annual Hawaii International Conference on System Sciences

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Parking shortage is a major problem in modern cities. Drivers cruising in search of a parking space directly translate into frustration, traffic congestion, and excessive carbon emission.We introduce a simple and effective deep learning-based parking space notification (PSN) system to inform drivers of new parking availabilities and re-occupancy of the freed spaces. Our system is particularly designed to target areas with severe parking shortages (i.e., nearly all parking spaces are occupied), a situation that allows us to convert the problem of detecting parking vacancies into recognizing vehicles leaving from their stationary positions. Our PSN system capitalizes on a calibrated Mask R-CNN model and a unique adaptation of the IoU concept to track the changes of vehicle positions in a video stream. We evaluated PSN using videos from a CCTV camera installed at a private parking lot and publicly available YouTube videos. The PSN system successfully captured all new parking vacancies arising from leaving vehicles with no false positive detections. Prompt notification messages were sent to users via cloud messaging services.

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“…While online smart parking systems offer great potential for compensating the aforementioned issues of urban parking [3,4], sensory perception of the real-time occupancy rate of available parking space is essential for the performance of smart parking systems. For this task, manifold approaches for providing efficient and reliable occupancy detection of parking space exist, including visual sensor networks with camera via image recognition [5,6], communication-based via vehicular ad hoc networks [7], crowd sensing by taxis and map/location information [8] or camera-based drone surveillance [9]. More conventional systems typically require parking space selective infrastructure, such as magnetic or infrared [10], or more recently radio-based sensors [11].…”

Section: Introductionmentioning

confidence: 99%

Multipath-assisted Radio Sensing and Occupancy Detection for Smart In-house Parking in ITS

Ninnemann¹,

Schwarzbach²,

Michler³

2022

Preprint

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Joint, radio-based communication, localization and sensing is a rapidly emerging research field with various application potentials. Greatly benefiting from these capabilities, smart city, mobility, and logistic concepts are key components for maximizing the efficiency of modern transportation systems. In urban environments, both the search for parking space and freight transport are time-and space-consuming and present the bottlenecks for these transportation chains. Providing location information for these heterogeneous requirement profiles (both active and passive localization of objects), can be realized by using retrofittable wireless sensor networks, which are typically only deployed for active localization. An additional passive detection of objects can be achieved by assessing signal reflections and multipath properties of the transmission channel stored within the Channel Impulse Response (CIR). In this work, a proof-of-concept realization and preliminary experimental results of a CIR-based occupancy detection for parking lots are presented. As the time resolution is dependent on available bandwidth, the CIR of Ultra-wideband transceivers are used. For this, the CIR is smoothed and time-variant changes within it are detected by performing a background subtraction. Finally, the reflecting objects are mapped to individual parking lots. The developed method is tested in an in-house parking garage. The work provided is a foundation for passive occupancy detection, whose capabilities can prospectively be enhanced by exploiting additional physical layers, such as 5G or even 6G.

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Intelligent drone-based surveillance: application to parking lot monitoring and detection

Cited by 26 publications

References 10 publications

A Smart, Efficient, and Reliable Parking Surveillance System With Edge Artificial Intelligence on IoT Devices

A Smart, Efficient, and Reliable Parking Surveillance System With Edge Artificial Intelligence on IoT Devices

Deep Learning Based Parking Vacancy Detection for Smart Cities

Multipath-assisted Radio Sensing and Occupancy Detection for Smart In-house Parking in ITS

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