Hierarchical Feature Extraction for Traffic Vehicle Localization
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing computer vision tasks for correspondence estimation between images, such as traffic scenario detection, face challenges with hand-crafted descriptors and the trade-offs between shallow and deep feature extraction in convolutional neural networks (CNNs), where shallow features are sensitive to local structures but lack robustness, and deep features are robust but less precise.
Innovation Solution
A hierarchical feature extractor in CNNs is employed to concurrently extract geometric and semantic features from multiple layers, refining correspondences between images to generate accurate 3D location estimates of vehicles in traffic environments, using a Siamese structure with deep supervision and hard negative mining for improved feature descriptor learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If shallow feature extraction is used in CNNs, then geometric precision is improved, but robustness to appearance changes deteriorates
Solution Approach 1:
The patent combines shallow geometric features and deep semantic features into a unified correspondence estimation framework. The hierarchical feature extractor merges features from multiple CNN layers, allowing the system to simultaneously achieve geometric precision from shallow layers and robustness to appearance changes from deep layers through feature fusion and joint optimization.
Solution Approach 2:
The patent extends the feature extraction from a single dimension (either shallow or deep) to multiple dimensions by utilizing features from multiple CNN layers simultaneously. This multi-level feature hierarchy allows the system to capture both local geometric structures and global semantic information, resolving the trade-off between precision and robustness.
2Reliability
If deep feature extraction is used in CNNs, then robustness to appearance changes is improved, but geometric precision deteriorates
Solution Approach 1:
The patent merges deep semantic features with shallow geometric features in a unified correspondence estimation framework. The hierarchical feature extractor combines features from multiple CNN layers, allowing the system to simultaneously achieve robustness from deep layers and geometric precision from shallow layers through feature fusion.
Solution Approach 2:
The patent transitions from single-dimensional feature extraction to multi-dimensional feature extraction by utilizing features from multiple CNN layers simultaneously. This hierarchical approach captures both global semantic robustness and local geometric precision, resolving the trade-off between these two properties.
3Device complexity
If hand-crafted descriptors are used for correspondence estimation, then system complexity is reduced, but performance accuracy deteriorates
Solution Approach 1:
The patent replaces hand-crafted descriptor methods with learned feature extractors based on convolutional neural networks. This substitution transitions from manual feature engineering to automated learning-based feature extraction, significantly improving correspondence estimation accuracy while the modular CNN architecture keeps the system complexity manageable.
Data Source
AI summary
Systems and methods for detecting traffic scenarios include an image capturing device which captures two or more images of an area of a traffic environment with each image having a different view of vehicles and a road in the traffic environment. A hierarchical feature extractor concurrently extracts features at multiple neural network layers from each of the images, with the features including geometric features and semantic features, and for estimating correspondences between semantic features for each of the images and refining the estimated correspondences with correspondences between the geometric features of each of the images to generate refined correspondence estimates. A traffic localization module uses the refined correspondence estimates to determine locations of vehicles in the environment in three dimensions to automatically determine a traffic scenario according to the locations of vehicles. A notification device generates a notification of the traffic scenario.