Forward Vehicle Detection With Adaptive Search and Feature Tracking
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Solution Overview
Problem
Current vehicle detection systems in advanced driver assistance systems (ADAS) face challenges in accurately and stably detecting forward vehicles, leading to potential collisions and interruptions in augmented reality navigation services.
Innovation Solution
A method involving continuous image capture, adaptive search area setting based on previous vehicle detection, and feature point tracking using machine learning models to enhance vehicle detection and tracking, even when initial detection fails, ensuring reliable forward vehicle collision warnings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If vehicle detection is performed using machine learning models on the entire image, then detection accuracy is improved, but processing speed deteriorates
Solution Approach 1:
The image processing is segmented into two stages: first, a coarse detection phase using a machine learning model to identify potential vehicle regions, and second, a refinement phase using feature point tracking only within the detected search area. This segmentation allows the computationally intensive ML model to process only relevant portions of the image, improving both speed and accuracy.
Solution Approach 2:
The system performs preliminary vehicle detection using the machine learning model to establish an initial search area before applying feature point tracking. By pre-defining the search boundaries based on ML detection results, the system avoids processing the entire image with both methods, thus improving processing speed while maintaining detection accuracy through the subsequent tracking refinement.
2Stability of the object's composition
If feature point tracking is performed continuously to maintain vehicle location, then tracking stability is improved, but computational resources are consumed even when detection succeeds
Solution Approach 1:
The system dynamically adjusts the tracking operation based on detection results. When vehicle detection succeeds and reliability is high, feature point tracking is terminated to save computational resources. When detection fails or reliability is low, tracking is performed to maintain stability. This dynamic approach balances tracking stability with energy efficiency.
Solution Approach 2:
The system uses feedback from the vehicle detection results to control the tracking operation. The detection reliability serves as a feedback signal that determines whether to continue or terminate tracking. This feedback mechanism ensures tracking stability only when necessary, optimizing computational resource usage based on actual detection needs.
3Reliability
If search area is enlarged to ensure vehicle detection, then detection reliability is improved, but processing time increases
Solution Approach 1:
The system performs preliminary detection with an initial search area to quickly identify potential vehicle locations. Only after this preliminary action succeeds does the system enlarge the search area for refined detection. This two-stage approach ensures detection reliability through enlarged search when needed, while minimizing processing time by using the smaller initial search area for quick screening.
Data Source
AI summary
There is provided a method for detecting a vehicle including receiving continuously captured front images, setting a search area of the vehicle in a target image based on a location of the vehicle or a vehicle area detected from a previous image among the front images, detecting the vehicle in the search area according to a machine learning model, and tracking the vehicle in the target image by using feature points of the vehicle extracted from the previous image according to a vehicle detection result based on the machine learning model. Since the entire image is not used as a vehicle detection area, a processing speed may be increased, and a forward vehicle tracked in an augmented reality navigation may be continuously displayed without interruption, thereby providing a stable service to the user.