Dynamic Lane Recognition via Vehicle Trajectory Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current lane recognition systems in intelligent transportation rely on manual marking of lane positions in videos, which can lead to inaccurate analysis of traffic events when the camera angle changes, as the manual markings may not adapt promptly to these changes.
Innovation Solution
A method and apparatus for dynamic lane recognition using vehicle trajectories obtained from video frames, where a vehicle detection model identifies vehicle positions and trajectories, allowing for real-time recognition of lane types and attributes without manual pre-checking, even if the camera angle changes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manual marking of lane positions is used in advance, then the system complexity is reduced and ease of operation is improved, but the reliability of lane recognition deteriorates when camera angles change
Solution Approach 1:
The patent transitions from static manual lane markings to dynamic lane recognition that automatically adapts to camera angle changes. The system continuously updates lane positions based on real-time video analysis, making the lane recognition system dynamic rather than static, thereby maintaining reliability under varying camera conditions
Solution Approach 2:
The system implements feedback by continuously analyzing vehicle trajectories and video frames to detect lane positions. When camera angle changes occur, the system receives feedback from trajectory deviations and automatically adjusts lane position markings, creating a closed-loop control system that maintains accuracy without manual intervention
2Productivity
If manual pre-check of video images is performed, then the productivity of lane recognition is improved, but the measurement precision of lane position deteriorates when shooting angle changes
Solution Approach 1:
The system enables self-service by automatically performing lane recognition without requiring manual pre-check of video images. The algorithm independently analyzes vehicle trajectories and video frames to determine lane positions, eliminating the need for human operators to manually mark lanes while maintaining high measurement precision through continuous adaptive recognition
3Reliability
If dynamic lane recognition based on vehicle trajectories is implemented, then the reliability of lane recognition is improved, but the device complexity increases
Solution Approach 1:
The patent replaces manual mechanical marking operations with automated computational algorithms. Instead of human operators physically marking lane positions, the system uses computer vision algorithms to detect vehicle trajectories and automatically calculate lane positions, substituting mechanical human labor with automated digital processing while improving reliability
Solution Approach 2:
The system changes parameters by transitioning from fixed manual lane position data to dynamically calculated positions based on vehicle trajectory parameters. The algorithm continuously adjusts lane position parameters based on observed vehicle movement patterns, enabling adaptive recognition that maintains reliability under varying conditions despite increased computational complexity
Data Source
AI summary
A method for lane recognition includes, when recognizing a lane in a video, obtaining the video recorded by a monitoring device set up on a road, where the video records a plurality of vehicles running on the road; determining positions of each vehicle in a plurality of video frames of the video; determining a vehicle trajectory of each vehicle in the video based on the positions of the vehicle in the video frames of the video; and recognizing at least one lane in the video based on vehicle trajectories of the vehicles in the video.