Parking slot dataset for different scenes

Data models related to the Smart Cities Domain. Includes data models for Buildings, Parking, Urban Mobility & etc.

🅿️ open API serving parking lot data for multiple cities

# Intelligence traffic monitoring system ### About Due to a huge number of vehicles ,very busy road and parking which may not be possible manually as a human being, tends to get fatigued due to monotonous nature of the job and they cannot keep track of the vehicles when there are multiple vehicles are passing in a very short time. So modern cities need to establish effective automatic systems for traffic management and scheduling. The objective of this project is to design and develop an accurate and automatic number plate recognition system, Automatic traffic light control using google Api live traffic density data, smart fine system and also We can track the lost vehicle using vehicle number plate detection and find its location by google Map API. Intelligent Traffic Monitoring System (ITMS) is an image processing and machine learning technology to identify vehicles by their license plates and we uses the microService of google API for live traffic density. ### Features 1. License plate number recognition. 2. Matching the plate number with Database. 3. Intelligence traffic light control using live traffic density data. 4. Show traffic density of particular area for some duration of month in form of graph. 5. Online Vehicle license registration. 6. Smart fine system. ###Applications 1. Automated track the location of stolen vehicle 2. Anti-Theft/ Vehicle detection. 3. Traffic light automation ,no requirement of Traffic police. 4. Smart fine /E Challan Systems. 5. Car Parking / Automatic Toll Deduction. 6. Law Enforcement 7. VIP/Ambulance path Clearance 8. Help the government to take ● Increase the efficiency of existing transport infrastructure ● Develop a license plate recognition system, ● Build a smart fine system and in future enhancement automated fine systems for vehicles. ● Live Traffic detection system and automated traffic light control system. ● Predict the traffic density using machine learning for specific areas by its previous data. ● Automated lost vehicle detection system and information to administration. ● Handle traffic congestion using automated light control system. ### Installation * Clone the project. * Run `yarn install` to install the dependencies. * Run `yarn start` to view the project in action. ### OpenCV Demo to Count Vehicles * In "countingCars" directory, run 'python count.py' . ### License plate detection go to vehicle_number_by_its_pate folder and type python3 licenseplateDetection.py 1.jpg #secreenshot <img src="./screenshot/IMG_20200901_103735.jpg"> <img src="./screenshot/IMG_20200901_103751.jpg"> <img src="./screenshot/IMG_20200901_103811.jpg"> <img src="./screenshot/IMG_20200901_103826.jpg"> <img src="./screenshot/IMG_20200901_103844.jpg"> <img src="./screenshot/IMG_20200901_103906.jpg"> <img src="./screenshot/IMG_20200901_103943.jpg"> <img src="./screenshot/IMG_20200901_104003.jpg"> <img src="./screenshot/IMG_20200901_104044.jpg"> <img src="./screenshot/IMG_20200902_032314.jpg">

Python script to respond to tweets about traffic violations using data about parking and camera violations from https://opendata.cityofnewyork.us/

This is a College project app, Project name : Smart car parking Station with Mobile application in this project we make a station where when any car came in parking slot then sensor will send data to the nodemcu and Nodemcu will send data to firebase and you can see live update of parking system int his app

arXiv 2011.00413: (MATLAB Simulation) Collision Avoidance in Tightly-Constrained Environments without Coordination: a Hierarchical Control Approach.

Visualization of parking data to assist in understanding of the effects of parking policies on a neighborhood by neighborhood basis in the City of Los Angeles

This is the code to detect the parking space for the car given 2D image from the google maps and 3D point Cloud data of the current enivornment.

Project to map Chicago parking tickets based on FOIA data.

Find a free parking in the nearest residential street when driving in Montréal. A Montréal Open Data project.

TILDA provides access to bicycle and parking infrastructure data from OpenStreetMap (OSM) for administrative staff.

The code and data of the paper "PewLSTM: Periodic LSTM with Weather-Aware Gating Mechanism for Parking Behavior Prediction"

Processing pipeline to generate data on public parking from OpenStreetMap-Data.

IOU Calculation for 2D Quadrilaterals The major functional components of autonomous vehicles are perception, control, planning, system management, and localization. Perception is a process that senses the surrounding environment using various sensors like Radars, LiDARs, Ultrasonic and Cameras sensors. Sensors are designed to extract information from the environment and hence, to perceive the surroundings. • Lidars are used to extract the information on the position and shape of surrounding obstacles within its range and field of view (FOV). • Camera sensor data provides information about the object class. • Radars are used to derive the position and velocity of the obstacles and so on. Multi-sensor fusion integrates the sequence of observations from a number of heterogeneous sensors into a single best estimate of the state of the environment. One of the Sensor Fusion outputs is the IOU (Intersection over Union) or Jaccard index during the object detection. When the object detection is performed through more than one source of sensors (such as Ultrasonic and Camera sensors), the IOU or Jaccard index is calculated to quantify the percent overlap from two different sources of sensors. The basic problem in multi-sensor fusion systems is to integrate a sequence of observations from a number of different sensors into a single best estimate of the state of the environment. In such a case, the IOU helps to identify the overlap area, which is captured from the multi-sensors. For example for the Autonomous Parking Functionality of ADAS (Autonomous Driving Assistance System), the Ultrasonic and Camera sensors are capturing the free space for Ego Vehicle Parking (as shown in the below figure). As per the capability and mounting position of different sensors, the available parking space is captured. The captured area from different sensors may or may not be the same. In that case, the IOU or Jaccard Index helps to quantify the overlap area detected by two different sensors. Figure 1: Practical use case of IOU The IOU or Jaccard Index is calculated as follows: Figure 2: IOU Calculation The IOU (Intersection over Union) value varies between 0 to 1. More the overlap region better the IOU value. Henceforth the confidence in the input data from the sensors increases. Lower the IOU, troubles in deciding the available space for the parking as different sensors are showing different spaces for parking. Figure 3: Confidence decision based on IOU Note: Decision of the High Confidence from calculated IOU value varies from application to application. For example, in some applications, High Confidence can be decided over 0.8 IOU value whereas, in some other applications, High Confidence can be decided over 0.9 IOU value. The IOU calculation can be done over the images or coordinates captured from the different sensors. In addition, the IOU calculation can also be performed considering the captured object as a 2D or 3D object. In this article, I have focused on the IOU calculation based on coordinates received from two different sensors. The captured coordinates would be of 2D Quadrilateral. Refer to the MATLAB Code for the Calculation of IOU using the X and Y coordinates captured from the two different sensors. The point of interest here is in finding the intersection points and identifying the quadrilateral vertices that lie inside another quadrilateral. A glimpse of the MATLAB code results: Figure 4: IOU calculation from MATLAB Code I have considered all the possible conditions for regular/irregular quadrilateral such as complete overlapping, no overlapping, vertices having negative and positive coordinates, and so on. Thank you for reading. I am open to discussion on this topic. Do reach out to me at chetan9chudhari@gmail.com. HAPPY LEARNING!!!

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Django-based REST API for processing parking data

A WRLD3D js app demonstrating how to implement POI cards and displaying Parking slots availability data

Vehicle parking management system for my college which is efficient and user friendly. It is a web oriented project which uses PHP for connection and MySQL as backend.

Open Source Parking Spot Detection using satellite/drone vision data

Information about parking spaces generated from OpenStreetMap Data.

Analyze New York City parking signs data.

Where to build new bicycle parking spots in Paris? Supporting data-driven decision making with open data

Parking information and mapping data for five North American cities

historic archive of free parking places across germany

A smart parking system with real time data visualisation

This is a personal project that displays various forms of parking citation data at my university..

This application has been developed with the core goal to provide a 3D visualisation and experience to the users. By using it the users can “fly” around this 3D model of a city and visualise the effect of real-time data in the model. The map has been integrated with the CityPulse framework for displaying events aroud the city of Aarhus on traffic, parking, pollution and noise.

Parking data explorer web interface

数据结构课程设计 停车场管理系统 大学编程作业（TUST 天津科技大学 2022 年）