Edge-Based Parking Violation Detection Using Smartphone Cameras
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Solution Overview
Problem
Conventional systems for detecting on-street parking violations are inefficient, particularly in developing countries, due to challenges such as irregular no-parking sign deployment, non-standard parking practices, and the need for extensive sensing infrastructure, which leads to scalability and maintenance issues, as well as privacy concerns with centralized data processing.
Innovation Solution
An edge-based monitoring system using a smartphone's rear camera for visual sensing and location analytics, which processes video feeds in real-time to identify no-parking signs and vehicles, and sends status reports to a cloud server for parking violation detection, leveraging deep learning models and GPS for accurate measurement and compliance with parking policies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If centralized data processing is used for parking violation detection, then detection accuracy can be improved, but privacy concerns and computational load increase
Solution Approach 1:
The patent extracts and processes only essential violation-related data (vehicle presence, sign compliance) locally at edge devices, sending only status reports to the cloud server rather than transmitting complete video feeds. This extraction approach maintains detection accuracy while minimizing privacy risks by removing unnecessary personal information from transmitted data.
Solution Approach 2:
The patent introduces an intermediary processing layer at edge devices (smartphones, cameras) that acts as a mediator between data collection and centralized processing. These edge devices perform initial analysis and filtering, transforming raw video data into structured status reports before cloud processing, thereby reducing computational load and privacy concerns while preserving detection accuracy.
2Area of stationary object
If extensive sensing infrastructure is deployed for parking violation detection, then detection coverage is improved, but scalability and maintenance issues worsen
Solution Approach 1:
The patent makes existing smartphones and cameras multi-functional by enabling them to perform parking violation detection in addition to their primary functions. This universal approach eliminates the need for dedicated sensing infrastructure, improving detection coverage while reducing infrastructure complexity and maintenance burden.
Solution Approach 2:
The patent enables self-service deployment where citizens can install detection applications on their own smartphones without requiring specialized infrastructure. This approach expands detection coverage across the city while minimizing infrastructure complexity, as each device independently performs detection and reports to the cloud server.
3Productivity
If real-time video processing is performed, then violation detection speed is improved, but computational effort increases
Solution Approach 1:
The patent segments the processing workload between edge devices and cloud servers. Edge devices perform lightweight real-time processing to generate status reports, while the cloud server handles complex analysis. This segmentation enables real-time detection speed at the edge while distributing computational effort to reduce energy consumption on individual devices.
Solution Approach 2:
The patent applies partial processing at the edge level, performing only essential detection tasks (vehicle detection, sign recognition) locally to achieve real-time response, while leaving more computationally intensive tasks for cloud processing. This partial action approach balances detection speed with computational efficiency.
Data Source
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AI summary
This disclosure relates generally to method and system for detecting on-street parking violations. The method include capturing, by using an media capturing device embodied in an electronic device mounted in a vehicle, a video stream of a scene during a trip of the vehicle. The video stream is processed at the electronic device to identify target objects such as no-parking signage and vehicles parked in the vicinity thereof. A meta-information associated with the target objects is stored in form of a short-term historian in a repository associated with the electronic device. The absolute locations of the target objects is determined and the historian is updated with the values of the absolute locations. A set of unique target objects is determined from amongst the target objects and a meta-information associated with the unique objects is sent to a cloud server for determining parking violations.