Vehicle Detection via Dynamic Valid Area and Weak Classifiers
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Solution Overview
Problem
Current intelligent driving assisting systems face high computational loads and prolonged processing times due to detecting vehicles in the entire image, which includes unnecessary areas like sky and buildings, rather than focusing on the valid region where vehicles are present.
Innovation Solution
The method involves determining a valid area in the image based on lane line positions and vehicle velocity, using preset weak classifiers and thresholds to detect vehicles within this area, thereby reducing the computational load and processing time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If vehicle detection is performed across the entire image, then detection coverage is comprehensive, but computational load increases and processing time is prolonged
Solution Approach 1:
The patent divides the image into multiple regions of interest (ROIs) based on lane line detection results. By segmenting the image into valid detection areas (above the horizon line and within lane boundaries) and excluding irrelevant areas (sky, buildings, road surface), the system performs vehicle detection only in necessary regions, reducing computational load while maintaining detection coverage.
Solution Approach 2:
The patent extracts and identifies the valid detection area by detecting lane lines and determining the horizon line position. It removes irrelevant regions (sky area above horizon, road area below horizon) from the detection process, keeping only the meaningful regions where vehicles may appear, thus reducing unnecessary computations.
2Productivity
If the valid area is reduced based on lane lines and velocity, then processing time is shortened, but detection accuracy may be compromised
Solution Approach 1:
The patent dynamically adjusts the valid detection area based on real-time vehicle velocity. The lower boundary of the valid area is calculated using the formula: lower_boundary = horizon_line + velocity * time_threshold. This dynamic adjustment ensures that the detection area adapts to different driving conditions, maintaining detection accuracy while optimizing processing speed for each specific scenario.
Solution Approach 2:
The patent performs preliminary lane line detection and horizon line identification before vehicle detection. By pre-establishing the valid detection area boundaries based on lane geometry and vehicle velocity, the system prepares the detection region in advance, ensuring that no potential vehicle targets are excluded while minimizing the detection area for efficiency.
Data Source
AI summary
The disclosure discloses a method for detecting a vehicle in a driving assisting system. The method for detecting a vehicle in a driving assisting system includes: obtaining an image to be detected, and determining the positions of lane lines in the image to be detected; determining a valid area in the image to be detected, according to the positions of the lane lines, and the velocity of the present vehicle; and determining a detected vehicle in the valid area according to T preset weak classifiers, and thresholds corresponding to the respective weak classifiers, wherein T is a positive integer.