Parallel Road Scene Primitive Detection Using Multi-Core Processing
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Solution Overview
Problem
Existing approaches for detecting road scene primitives using multiple cameras are hardware-intensive, time-consuming, and fail to exploit modern many-core processing architectures, leading to computational inefficiency and accuracy issues.
Innovation Solution
A parallel processing method using a vehicle's camera system with at least two parallel processing cores, employing machine learning and computer vision techniques to generate multiple views of road scene primitives, such as pedestrians, traffic signs, and road features, and identifying them in real-time using convolutional neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If existing approaches for detecting road scene primitives using multiple cameras are used, then detection capability is achieved, but hardware complexity and computational load increase significantly
Solution Approach 1:
The patent segments the detection task by processing different camera views in parallel using multiple processing cores. Each core handles specific views independently, dividing the complex multi-camera processing into manageable parallel tasks that reduce overall system complexity while maintaining detection capability
Solution Approach 2:
The patent transitions from sequential single-core processing to parallel multi-core processing, adding a temporal dimension to the computation. This dimensional change allows simultaneous processing of multiple camera views, reducing hardware complexity requirements while maintaining reliability
2Reliability
If existing multi-camera detection approaches are used, then road scene primitives can be detected, but processing time increases
Solution Approach 1:
The patent implements periodic parallel processing where multiple processing cores periodically process different camera views simultaneously. This periodic parallel action reduces total processing time while maintaining detection accuracy through comprehensive view analysis
Solution Approach 2:
The patent ensures continuous useful action by keeping all processing cores actively engaged in detecting different road scene primitives across multiple views. This continuous parallel processing eliminates idle time and reduces overall processing duration while maintaining high detection accuracy
3Device complexity
If modern many-core processing architectures are not exploited, then hardware simplicity is maintained, but computational efficiency decreases
Solution Approach 1:
The patent introduces dynamic parallel processing that adapts to the capabilities of modern many-core architectures. The system dynamically distributes camera view processing across available cores, transforming static simple hardware into a dynamically efficient processing system that achieves high computational efficiency
Solution Approach 2:
The patent changes the processing parameter from sequential single-core operation to parallel multi-core operation. This parameter change enables the system to exploit modern many-core architectures, dramatically improving computational efficiency while maintaining hardware simplicity through software-based parallelization
Data Source
AI summary
Techniques for road scene primitive detection using a vehicle camera system are disclosed. In one example implementation, a computer-implemented method includes receiving, by a processing device having at least two parallel processing cores, at least one image from a camera associated with a vehicle on a road. The processing device generates a plurality of views from the at least one image that include a feature primitive. The feature primitive is indicative of a vehicle or other road scene entities of interest. Using each of the parallel processing cores, a set of primitives are identified from one or more of the plurality of views. The feature primitives are identified using one or more of machine learning and classic computer vision techniques. The processing device outputs, based on the plurality of views, result primitives based on the plurality of identified primitives from multiple views based on the plurality of identified entities.