Monocular Traffic Scene Recognition via Atomic Hierarchical Decomposition
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Solution Overview
Problem
Conventional collision avoidance systems in automobiles struggle with complex traffic scenarios involving multiple simultaneous scenes and a vast number of possible scene types, requiring real-time semantic recognition to enhance advanced warning systems effectively.
Innovation Solution
A system that employs monocular traffic scene recognition using a hierarchical model to decompose scenes into atomic and high-order scenes, leveraging 3D object localization and structured support vector machines for real-time inference, enabling robust and scalable detection of complex traffic scenarios from a single camera.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional collision avoidance systems are used to detect objects, then basic object detection is achieved, but semantic recognition of traffic scenes is insufficient
Solution Approach 1:
The patent segments traffic scene recognition into two hierarchical levels: atomic scenes (basic scene types like pedestrian crossing, intersection) and high-order scenes (complex scenarios combining multiple atomic scenes). This segmentation allows the system to process semantic information in manageable units, reducing the overall complexity while improving semantic recognition capability.
Solution Approach 2:
The patent introduces atomic scenes as intermediary concepts between basic object detection and complex high-order scene recognition. These atomic scenes serve as building blocks that bridge the gap between simple object detection and comprehensive semantic understanding, enabling gradual information integration without overwhelming system complexity.
2Adaptability or versatility
If the system recognizes a vast number of possible scene types, then comprehensive scene coverage is achieved, but real-time processing becomes difficult
Solution Approach 1:
By dividing the vast number of scene types into a limited set of atomic scenes and combinatorially forming high-order scenes, the system achieves comprehensive coverage without processing all scene types simultaneously. The hierarchical structure enables efficient real-time processing by focusing on identifying atomic scenes first, then combining them as needed.
Solution Approach 2:
The patent implements a nested hierarchical structure where atomic scenes are nested within high-order scenes. This nesting allows the system to maintain a compact representation of diverse traffic scenarios, where a small number of atomic scene types can组合 into many high-order scenes, thereby achieving versatile scene coverage with limited processing complexity.
3Loss of information
If multiple simultaneous scenes with several participants are detected, then comprehensive scene understanding is achieved, but system complexity increases significantly
Solution Approach 1:
The patent segments complex multi-scene detection into independent atomic scene detectors, each handling a specific atomic scene type. This segmentation allows the system to process multiple simultaneous scenes by independently identifying atomic scenes and then integrating them, reducing the complexity of the overall recognition model while preserving comprehensive scene context.
Solution Approach 2:
The hierarchical model framework serves as a universal structure that can handle various combinations of atomic scenes and participants. This multi-functional framework accommodates diverse traffic scenarios without requiring separate specialized models for each complex situation, thereby reducing overall system complexity while maintaining comprehensive understanding.
Data Source
AI summary
Systems and methods are disclosed to provide an Advanced Warning System (AWS) for a driver of a vehicle, by capturing traffic scene types from a single camera video; generating real-time monocular SFM and 2D object detection from the single camera video; detecting a ground plane from the real-time monocular SFM and the 2D object detection; performing dense 3D estimation from the real-time monocular SFM and the 2D object detection; generating a joint 3D object localization from the ground plane and dense 3D estimation; and communicating a situation that requires caution to the driver.