OCR-RTPS: an OCR-based real-time positioning system for the valet parking

Wu, Zizhang; Chen, Xinyuan; Wang, Jizheng; Wang, Xiaoquan; Gan, Yuanzhu; Fang, Muqing; Xu, Tianhao

doi:10.1007/s10489-022-04362-x

Cited by 4 publications

(2 citation statements)

References 64 publications

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“…[48], Bassam et al presented an IoT system designed to detect available parking spaces in urban areas. Following this concept, Wu et al in [49] proposed a vehicle localization system to detect parking space numbers in real time. Both studies employed cameras placed in parking lots and implemented an Optical Character Recognition (OCR) model to extract the textual information about available parking spaces.…”

Section: Use Cases In Iot Applications and Characteristics Overviewmentioning

confidence: 99%

A Survey of AI Techniques in IoT Applications with Use Case Investigations in the Smart Environmental Monitoring and Analytics in Real-Time IoT Platform

Panduman,

Funabiki,

Fajrianti

et al. 2024

Information

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In this paper, we have developed the SEMAR (Smart Environmental Monitoring and Analytics in Real-Time) IoT application server platform for fast deployments of IoT application systems. It provides various integration capabilities for the collection, display, and analysis of sensor data on a single platform. Recently, Artificial Intelligence (AI) has become very popular and widely used in various applications including IoT. To support this growth, the integration of AI into SEMAR is essential to enhance its capabilities after identifying the current trends of applicable AI technologies in IoT applications. In this paper, we first provide a comprehensive review of IoT applications using AI techniques in the literature. They cover predictive analytics, image classification, object detection, text spotting, auditory perception, Natural Language Processing (NLP), and collaborative AI. Next, we identify the characteristics of each technique by considering the key parameters, such as software requirements, input/output (I/O) data types, processing methods, and computations. Third, we design the integration of AI techniques into SEMAR based on the findings. Finally, we discuss use cases of SEMAR for IoT applications with AI techniques. The implementation of the proposed design in SEMAR and its use to IoT applications will be in future works.

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Section: Use Cases In Iot Applications and Characteristics Overviewmentioning

confidence: 99%

A Survey of AI Techniques in IoT Applications with Use Case Investigations in the Smart Environmental Monitoring and Analytics in Real-Time IoT Platform

Panduman,

Funabiki,

Fajrianti

et al. 2024

Information

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show abstract

“…However, it requires the establishment of a 3D model before implementing positioning. Wu et al [21] proposed a real-time positioning system based on detecting parking numbers. Its application range is limited, and it is not suitable for indoor use.…”

Section: Recent Advances In Image-based Localization Techniquesmentioning

confidence: 99%

Multi-granularity and Multi-modal Feature Fusion for Indoor Positioning

Pei,

Wang,

Zhao

et al. 2024

Preprint

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Despite the widespread use of indoor positioning technology, current WiFi positioning methods face challenges in accuracy and efficiency due to the complexities of indoor environments and the intricate architecture resulting from pre-deployed Access Points (APs). Moreover, while visual image positioning methods are effective, their costliness stemming from the requirement of using binocular cameras to obtain depth image data hinders their widespread adoption. To tackle these challenges, we propose an indoor positioning method that leverages multi-granularity and multi-modal feature fusion. In the initial, coarse-grained positioning phase, a neural network model is trained using image data to roughly localize indoor scenes. This model adeptly captures the geometric relationships within images, providing a foundation for more precise localization in subsequent stages. In the fine-grained positioning stage, Channel State Information (CSI) features from WiFi signals and Scale-Invariant Feature Transform (SIFT) features from image data are fused, creating a rich feature fusion fingerprint library that enables high-precision positioning. Experimental results show that our proposed method synergistically combines the strengths of WiFi fingerprint and visual positioning, resulting in a substantial enhancement of positioning accuracy. Specifically, our approach achieves an accuracy of 0.4 meters for 45\% of positioning points and 0.8 meters for 67\% of points. Overall, this approach charts a promising path forward for advancing indoor positioning technology.

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TRINS: Towards Multimodal Language Models that Can Read

Zhang,

Chen

et al. 2024

2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

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OCR-RTPS: an OCR-based real-time positioning system for the valet parking

Cited by 4 publications

References 64 publications

A Survey of AI Techniques in IoT Applications with Use Case Investigations in the Smart Environmental Monitoring and Analytics in Real-Time IoT Platform

A Survey of AI Techniques in IoT Applications with Use Case Investigations in the Smart Environmental Monitoring and Analytics in Real-Time IoT Platform

Multi-granularity and Multi-modal Feature Fusion for Indoor Positioning

TRINS: Towards Multimodal Language Models that Can Read

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