2024年09月25日

i played brawlhalla for the first. looks fun enough. controls are still confusing though.

what’s interesting is that brawlhalla(?) or steam is able to do -multikeyboard support. so if you have a laptop keyboard and an external usb keyboard, it’s possible to have different outputs but the same inputs from different keyboards

not sure if this is a steam thing or just brawlhalla

TL;DRs

Automatic Number Plate Recognition System using Raspberry Pi

Implements license plate recognition system using python and opencv on raspberry pi…​ don’t use this paper

ACCURATE VEHICLE NUMBER PLATE REGOGNITION AND REAL TIME IDENTIFICATION USING RASPBERRY PI

Automatic License Plate Recognition system using OpenCV for online deployment and real-time processing on Raspberry Pi 3.

Year

2018

Remark

I can probably use this.

Notes

The authors implemented Raspberry Pi 3 for online process compared to MATLAB being offline.

Automated number plate recognition system

An automatic number-plate recognition system implemented through OpenCV.

Year

2021

Notes

  • Deployed in electronic toll collections, homes/offices to manage vehicle access in garages or premises, and traffic highways.

  • Used with database records of number plates which can be used for crime investigations.

  • Useful for law enforcement which records vehicles from the database

Automated License Plate Recognition for Resource-Constrained Environments

A hardware-efficient automated license plate recognition implemented on low-resource edge devices focusing on accuracy, energy efficiency, and computational latency.

Year

2022

Notes

  • Proof-of-concept prototype at nighttime

  • The methodology uses deep learning Neural Architecture Search

  • it is challenging to train the discovered deep neural networks to recognize license plates due to the lack of a large, annotated and diverse dataset

  • Simulates the detection of number plates to identify poachers by sending the text through SMS

  • Achieving over 90% accuracy cannot be executed on low-cost edge platforms such as raspberry pi

Devices

  • Raspberry Pi Zero

  • Raspberry Pi 3 Model B+

  • Raspberry Pi Camera Module

  • Intel Neural Compute Stick 2

Anonymous Vehicle Detection for Secure Campuses: A Framework for License Plate Recognition using Deep Learning
abstract

Automatic license plate recognition is being widely used for numerous applications since its inception. The ability to procure license plate numbers accurately has been beneficial in maintaining traffic rules, parking enforcement, and security. In this paper, we have discussed the results of using ALPR for recognition of anonymous vehicles entering our university campus. We used deep learning for license plate localization and Tesseract OCR for license plate recognition. By doing so we could read the license plates of vehicles entering a particular campus and verify if the vehicle is authorized by comparing it with a predefined list of authorized vehicles. To efficiently extract these number plates we have trained our model using Faster RCNN and tuned it to get the best output. The results of which have been discussed in this paper. Further, the image processing techniques used for preprocessing the identified number plate have been mentioned here. For character segmentation and character recognition, we have used tesseract. While training our model for number plate extraction the minimum loss obtained was 0.011 with RMSprop optimizer at initial learning rate 0.002.

conclusion

RMSprop gave the best result when the learning rate was set to 0.002. Applying this model, we could localize the license plate entering a given campus. Following which, with the help of above-mentioned filters we obtained a high contrast and noise-free image of the identified number plate and lastly with the help of Tesseract OCR we were able to extract the alpha-numeric characters from the plate in string format. In this proposed work change in light intensity of surroundings on number plates would make character recognition difficult. In future, changes could be made to the OCR to help accommodate light intensity on the number plate. This framework can further be used for smart traffic: identifying the number plates of vehicles breaking traffic rules or for optimized parking: keeping an account of vehicles in parking

Identification of Unauthorized Vehicles by License Plate Recognition Through Image Processing
abstract

The identification of vehicle’s license plates is becoming increasingly essential in the digital transportation system, particularly in Bangladesh. Improved safety, access to vehicles in authorized locations, time savings at tolls, and the discovery of guilty automobiles engaged in street accidents or other connected crimes are just a few of the benefits. This paper presents a simple method to identify the Bengali license plate used in the front and back sides of a registered vehicle through image processing. Number plate extraction, character segmentation, and character identification are the three fundamental pre-processing phases in the recommended approach. The test picture has been converted to gray level at the pre-processing stage. The proposed approach extracts the area from the license plate more effectively. Improved identification rate is found compared to some studies which has been carried out previously.

conclusion

This paper presents a simple method of identifying the unauthorized vehicles by recognizing the Bengali licence plate using image processing. The developed prototype model successfully identify the Bengali licence plate of Bangladeshi vehicles with a very good accuracy of 92% and 96%, respectively, depending on the night and day time. The detection approach is straightforward and practical with a promising future applications in the transportation sector of Bangladesh. The developed model can also be applied to recognized the number plate of any language