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.

It is challenging for motorists to manually search for a parking slot in a crowded parking area. Conventional methods for locating vacant or occupied space are regarded as inefficient and unreliable. In this work, an IoT-enabled vacant parking slot detection system is developed and tested using low-cost inkjet printed passive Ultra-High-Frequency (UHF) RFID tags. The working principle of the proposed system is to analyze the back-scattered signals strength from the tags at the receiver side for vehicle presence/absence detection in the parking slots. First of all, We propose a low-cost passive tag optimized to work in UHF RFID-band pasted on plastic material display for parking slots. The data collected by RFID reader from the tags is sent over Message Queuing Telemetry Transport (MQTT) protocol via Scotland 5G-network to MongoDB database using Python module Pymongo for decision making and display of vacant parking slots to the motorists. Python based web-app extracts data from the database and shows it on a web-app display using EJS for each slot. Experimental observations conducted at parking slots in the presence of vehicles indicate a drastic reduction in Received-Signal- Strength-Index (RSSI) value at the receiver end, which implies that RSSI signal drop can be utilized for the detection of vehicle presence in the parking slot. The suggested approach aims to facilitate the economical and effective location of available parking slots. The Test results for the proposed system are recorded and presented, demonstrating its significant performance and showing great accuracy for the tested scenarios.

The detection of head movement plays a crucial role in human-computer interaction systems. These systems depend on control signals to operate a range of assistive and augmented technologies, including wheelchairs for Quadriplegics, as well as virtual/augmented reality and assistive driving. Driver drowsiness detection and alert systems aided by head movement detection can prevent major accidents and save lives. Wearable devices, such as MagTrack consist of magnetic tags and magnetic eyeglasses clips and are intrusive. Vision-based systems suffer from ambient lighting, line of sight, and privacy issues. Contactless sensing has become an essential part of next-generation sensing and detection technologies. Wi-Fi and radar provide contactless sensing, however in assistive driving they need to be inside enclosures or dashboards, which for all practical purposes in this paper have been considered as through walls. In this study, we propose a contactless system to detect human head movement with and without walls. We used ultra-wideband(UWB) radar and Wi-Fi signals, leveraging machine and deep learning techniques. Our study analyzes the six common head gestures: right, left, up, and down movements. Time-frequency multi-resolution analysis based on wavelet scalograms is used to obtain features from channel state information values, along with spectrograms from radar signals for head movement detection. Feature fusion of both radar and Wi-Fi signals is performed with state-of-the-art deep learning models. A high classification accuracy of 83.33% and 91.8% is achieved overall with the fusion of VGG16 and InceptionV3 model features trained on radar and Wi-Fi time-frequency maps with and without the walls, respectively.

An IoT-enabled vehicle recognition system is developed and tested in this work, besides the tag antenna design and prototype for the vehicle windshield. Firstly, a flexible, surface-tolerant, low-profile, and low-cost passive UHF-RFID windshield tag inlay is designed. The tag is prototyped as wet-inlay with silver conductive-ink inkjet printed antenna on Polyethylene-Terephthalate layer, bonded with Higgs-EC IC. The dimension of the antenna is 0.25 $\times$ 0.05${\lambda }_0$. The overall inlay structure is around 0.27 $\times$ 0.06${\lambda }_0$. The antenna is designed at ETSI band, with a return loss of −14 dB at 900 MHz and a read range of 13.3 m and 8 m over lower and upper bands, respectively. The antenna demonstrates an omnidirectional radiation pattern and a maximum gain of 1.92 dBi. The developed prototype is tested in both indoor and outdoor environments. Secondly, the data is sent over the IoT cloud where decision-making is made for authorization. The reader data is transmitted over MQTT broker, via the Scotland 5G network to the MongoDB database using Python module Pymongo. The algorithm for vehicle recognition is written and verified by testing the dummy registered tags for access authorization, with a 100% recognition accuracy. We achieved higher read ranges, surface tolerance, and low profile features, comparatively. The overall system shows optimally great results.