Camera-Based Traffic Hand Signal Detection with Lightweight Processing
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Solution Overview
Problem
Conventional methods for recognizing traffic hand signals require significant computational resources and are limited in situations requiring depth image-based region extraction or skeleton imparting, making them inefficient for autonomous vehicles.
Innovation Solution
A traffic hand signal detection system using a camera-based approach that includes an imaging unit, an image classifier, a detection module, and an analysis module, utilizing neural networks like Vanilla RNN, LSTM, and GRU to classify and determine hand signals from photographed images, reducing computational demands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods (depth image-based region extraction, skeleton imparting) are used for hand signal recognition, then recognition accuracy may be improved, but computational resource requirements increase significantly and device complexity increases
Solution Approach 1:
The patent extracts only the necessary visual information from the image (hand region, arm motion direction) using simple thresholding and coordinate comparison, avoiding complex depth image processing and skeleton extraction. This selective extraction achieves sufficient recognition accuracy while dramatically reducing computational requirements.
Solution Approach 2:
The patent uses simple, lightweight computational operations (thresholding, bounding box comparison, basic coordinate calculations) instead of heavy computational methods. These simple operations are computationally inexpensive and can be executed efficiently in real-time without requiring complex processing pipelines.
2Measurement precision
If conventional methods (depth image-based region extraction, skeleton imparting) are used for hand signal recognition, then recognition accuracy may be improved, but processing time increases and productivity decreases
Solution Approach 1:
The patent extracts only the essential visual features (hand presence, arm motion direction) directly from the image without performing time-consuming depth image processing or skeleton extraction. This selective extraction enables fast processing while maintaining adequate recognition accuracy for autonomous vehicle applications.
Solution Approach 2:
The patent skips complex computational steps (depth processing, skeleton construction) and directly performs simple thresholding and coordinate comparison operations. This rushing through of unnecessary computational steps significantly reduces processing time and enables real-time hand signal recognition.
3Device complexity
If camera-based image classification is used for hand signal recognition, then computational overhead is reduced, but recognition accuracy in complex backgrounds may deteriorate
Solution Approach 1:
The patent applies local quality analysis by focusing computational attention only on the hand region and arm motion area identified through simple thresholding. By analyzing only the local characteristics (arm motion direction, hand position) rather than the entire image, the system achieves accurate recognition with minimal computational overhead, even in complex backgrounds.
Solution Approach 2:
The patent segments the image processing into distinct simple steps: thresholding to identify hand region, bounding box extraction, coordinate comparison for motion direction detection. This segmentation into simple, independent operations avoids the need for complex holistic image analysis while maintaining recognition accuracy.
Data Source
AI summary
A traffic hand signal detection system includes: an imaging unit configured to acquire a photographed image from a camera photographing a predetermined range; an image classifier configured to classify an arm motion from the photographed image provided from the imaging unit by imparting a class; a detection module configured to detect the arm motion from the photographed image classified by the image classifier and generate a traffic hand signal sequence converted into a number; and an analysis module configured to receive the traffic hand signal sequence converted into the number from the detection module and determine a type of a traffic hand signal.