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Computer Vision (CV) Technologies and Tools for Vision-based Cognitive IoT Systems
Published in Pethuru Raj, Anupama C. Raman, Harihara Subramanian, Cognitive Internet of Things, 2022
Pethuru Raj, Anupama C. Raman, Harihara Subramanian
Assisting Law Enforcement – The police departments across the USA use licence plate detectors to identify drivers with suspended licenses, catch stolen vehicles, or issue traffic citations. CV plays a very vital role here. Axon cameras are stuffed with CV capability to ensure public safety.
Automated Vehicle Number Plate Recognition System, Using Convolution Long Short-Term Memory Technique
Published in S. Poonkuntran, Rajesh Kumar Dhanraj, Balamurugan Balusamy, Object Detection with Deep Learning Models, 2023
S. Srinivasan, D. Prabha, N. Mohammed Raffic, K. Ganesh Babu, S. Thirumurugaveerakumar, K. Sangeetha
Deep learning approaches have produced good results in the computer vision area in recent years, notably for problems such as object detection, as well as identifying their class by providing a variety of deep network models [1–3]. These methods have opened the road for academics to employ strong deep learning models to build more performing algorithms and real-world systems, such as those used in license plate identification [4–7]. In smart intelligent systems, ALPR has received remarkable attention, and it is very popular because it can be applied in real-life case studies. By recognizing license plates, it is possible to identify or recognize vehicles by their unique registration numbers. The content in the number plate can be extracted using image processing techniques. The number plate content provides details such as the state and district where the motor vehicle was registered. The type of the vehicle, such as private, commercial, foreign, government or military, can be identified based on the number plate color and font color. We can get the vehicle owner’s name and address from the vehicle registration number. Identifying the owner of a vehicle may be useful when rules are violated. It can also be used [5]:To find blacklisted carsTo find vehicles crossing the limitsTo keep track of vehicle movements in order to combat crimeIn border control systemsIn traffic management decisionsTo understand behavior to detect anomaliesManually inspecting such a large number of moving cars is extremely difficult, which is why the development of an ALPR system that can be fast, accurate, and can also extract car numbers from moving vehicles is essential for the development of intelligent transportation systems.
Change in helmet use behavior enforced by CCTV cameras with automatic helmet use detection system on an urban arterial road
Published in Traffic Injury Prevention, 2020
Thaned Satiennam, Jetsada Kumphong, Wichuda Satiennam, Pongrid Klungboonkrong, Sittha Jaensirisak, Vatanavongs Ratanavaraha
The application of technology could increase the efficiency and effectiveness of traffic law enforcement. Our recent study (Wonghabut et al. 2018) applied CCTV cameras to develop and automatic helmet use detection system. Two CCTV cameras were installed at an intersection. A wide-angle CCTV camera was used to monitor motorcycles passing the intersection. If an unhelmeted motorcyclist was found, the narrow-angle CCTV camera was activated to capture images of the unhelmeted motorcycle user and the motorcycle license plate. The operating software was developed using C++ language and OpenCV library (i.e., a library of programming functions). Captured images were managed by MySQL database (an open-source relational database management system) for ticket issuance. There is, however, a lack of research on the effectiveness of the application of technology for helmet use enforcement. Further studies on the effectiveness of this technology in changing helmet use behavior are necessary.