Vehicle Light Signal Classification Using Semantic Keypoint Alignment
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Solution Overview
Problem
Conventional methods for classifying vehicle rear-side light signals, such as optical flow and SIFT flow, are computationally expensive and lack precision, making them inadequate for supporting autonomous vehicle decision-making.
Innovation Solution
A vehicle light signal classification system that uses semantic keypoints to localize turn and brake light signals, employing a first neural network to determine semantic keypoints and a second neural network to classify light signals based on difference images aligned by these keypoints.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional methods such as optical flow and SIFT flow are used for tracking bounding boxes across multiple images, then light signal classification can be performed, but computational cost becomes relatively high
Solution Approach 1:
The patent segments the vehicle light signals into distinct semantic keypoints (brake light keypoints and turn signal keypoints) rather than treating the entire bounding box as a single unit. This segmentation allows the system to focus computational resources on specific light signal regions, reducing overall computational cost while maintaining classification accuracy
Solution Approach 2:
The patent extracts semantic keypoints from the bounding box that specifically represent light signal locations. By taking out only the relevant light signal regions and their semantic keypoints from the full bounding box, the system reduces the amount of data that needs to be processed across multiple frames, thereby reducing computational cost
2Reliability
If conventional bounding box analysis is used for vehicle light signals, then classification can be performed, but precision is not sufficient to support autonomous system decision-making
Solution Approach 1:
The patent applies local quality by assigning specific semantic meanings to keypoints within the bounding box based on their spatial locations. Brake light keypoints are identified at specific rear regions while turn signal keypoints are identified at side regions. This local differentiation enables precise classification of different light signal types, providing the precision needed for autonomous decision-making
Solution Approach 2:
The patent transitions from conventional 2D bounding box analysis to a multi-dimensional representation by introducing semantic keypoints with spatial coordinates and semantic labels. This additional dimensional information (semantic meaning of each keypoint) enables more precise discrimination between different light signal types, improving detection precision for autonomous systems
Data Source
AI summary
A vehicle light classification system captures a sequence of images of a scene that includes a front/rear view of a vehicle with front/rear-side lights, determines semantic keypoints, in the images and associated with the front/rear-side lights, based on inputting the images into a first neural network, obtains multiple difference images that are each a difference between successive images from among the sequence of images, the successive images being aligned based on their respective semantic keypoints, and determines a classification of the front/rear-side lights based at least in part on the difference images by inputting the difference images into a second neural network.