Edge Computing Traffic Prediction via Object Tracking
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Solution Overview
Problem
Conventional traffic information systems require high-performance computing resources, large memory, and high-speed communication networks, leading to significant costs and inefficiencies.
Innovation Solution
A method and apparatus using edge computing devices adjacent to cameras to process and predict traffic information by detecting and tracking vehicle objects, determining movement trajectories, and counting vehicles by direction, reducing the need for centralized high-capacity systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a server device is used to analyze video data from multiple CCTV cameras, then traffic information can be detected and analyzed, but high-performance computing resources, large memory capacity, and high-speed communication networks are required, leading to high system construction costs
Solution Approach 1:
The patent segments the traffic information processing system into multiple edge computing devices distributed at different locations, each independently processing video data from local cameras. This divides the centralized server-based system into distributed units, reducing the need for high-performance centralized resources while maintaining overall detection accuracy through coordinated processing across multiple segments.
Solution Approach 2:
The patent introduces edge computing devices as intermediary components between CCTV cameras and the central server. These edge devices perform preliminary video analysis and extract traffic information locally, acting as mediators that reduce the computational burden on centralized servers and minimize the requirements for high-speed communication networks by transmitting only processed results rather than raw video data.
2Productivity
If video data from multiple devices is processed by a centralized server, then comprehensive traffic analysis can be performed, but massive data processing requirements demand high-performance computing resources and large memory capacity
Solution Approach 1:
The patent divides the video data processing task across multiple edge computing devices located at different positions, each handling local camera feeds independently. This segmentation distributes computational workload, preventing any single device from requiring excessive computing resources or memory capacity, while collectively maintaining comprehensive traffic monitoring coverage.
Solution Approach 2:
The patent implements preliminary video analysis and object detection at edge computing devices before data reaches the central server. By performing initial processing steps locally, the system reduces the volume and complexity of data requiring further processing, thereby decreasing overall computing resource consumption and memory requirements while preserving essential traffic information.
3Measurement precision
If a conventional server-based system is implemented, then traffic conditions can be analyzed, but high-speed communication networks are required to transmit and process massive video data
Solution Approach 1:
The patent positions edge computing devices as intermediaries that perform local video processing and extraction of traffic-relevant information. This intermediary processing step transforms raw video data into condensed traffic information (such as vehicle counts, speeds, and patterns) before transmission, dramatically reducing communication bandwidth requirements while preserving the accuracy needed for effective traffic condition analysis.
Solution Approach 2:
The patent extracts only the essential traffic information elements from complete video data at edge computing devices, such as vehicle presence, movement direction, and speed metrics. By extracting and transmitting only these critical parameters rather than entire video streams, the system maintains accurate traffic condition analysis capability while minimizing communication network speed requirements.
Data Source
AI summary
A method of predicting traffic information includes receiving a road image photographed by a vehicle that is moved on a road, and detecting an object for the vehicle from a plurality of frames included in the road image; tracking the object detected in the plurality of frames; checking a movement trajectory of the tracked object, and checking a movement direction corresponding to the movement trajectory of the object; and checking a number of vehicles for each movement direction on the basis of the checked movement direction.