Wide-View Object Detection via Synthetic Representation
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Solution Overview
Problem
Systems detecting objects in wide-view formats face challenges due to distortion and increased computational complexity, particularly in applications requiring high precision like automated driving, where fisheye cameras capture a broader field-of-view but result in distorted images at faraway areas, leading to lost precision during image stitching.
Innovation Solution
A detection system converts real wide-view images into synthetic representations using a style model, such as generative adversarial networks, to enhance feature extraction and object localization, employing a pyramid encoder for multi-scale feature extraction and a decoder for identifying attributes and bounding boxes, thereby reducing computational costs and maintaining precision without complex stitching.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple cameras are used to correct distortion in wide-view formats by stitching multiple views, then distortion correction is improved, but computational cost and complexity increase
Solution Approach 1:
The patent creates a synthetic representation (copy) of the wide-view image that preserves distortion characteristics, allowing the model to be trained on this simplified copy rather than requiring complex distortion correction of multiple real images. This copying approach maintains precision while reducing computational complexity.
Solution Approach 2:
The system performs preliminary action by training the detection model on synthetic representations that inherently account for distortion patterns. This pre-training on synthesized distorted views eliminates the need for complex real-time distortion correction during actual object detection, reducing computational complexity while maintaining precision.
2Measurement precision
If multiple cameras are used to correct distortion in wide-view formats by stitching multiple views, then distortion correction is improved, but precision is lost
Solution Approach 1:
Instead of stitching multiple real images which causes precision loss, the patent creates a synthetic representation that copies the essential geometric and distortion characteristics of the wide-view image. This synthetic copy preserves the necessary information for accurate object detection without the precision-degrading effects of stitching operations.
Solution Approach 2:
The patent changes the parameter representation by transforming real wide-view images into synthetic representations with modified geometric parameters that preserve distortion patterns. This parameter transformation allows the model to learn distortion characteristics directly without requiring precision-intensive stitching of multiple views.
3Measurement precision
If real data is used for training object detection in wide-view formats, then training accuracy is improved, but computational cost increases
Solution Approach 1:
The patent creates synthetic copies of training data that replicate the visual and geometric characteristics of real wide-view images. These synthetic training datasets can be generated once and reused, significantly reducing the computational cost of training while maintaining high accuracy, as the synthetic data preserves the essential distortion patterns and object appearances.
Solution Approach 2:
The system performs preliminary action by pre-generating synthetic training data that captures all necessary distortion patterns and scene variations. This pre-computed synthetic dataset eliminates the need for expensive real-time processing of real images during training, reducing computational cost while maintaining training accuracy through the preserved geometric and visual characteristics.
4Productivity
If a single wide-view image is used for detection, then computational cost is reduced, but distortion causes detection difficulties
Solution Approach 1:
The patent creates a synthetic representation that copies the distortion patterns of the single wide-view image in a controlled manner. This synthetic copy allows the detection model to learn how to handle distortion effectively, enabling accurate object detection in the original distorted image while maintaining the computational efficiency of using only a single image input.
Solution Approach 2:
The patent transforms the single distorted wide-view image into a synthetic representation with adjusted geometric parameters. This parameter change creates a version of the image where distortion effects are preserved but organized in a way that makes them learnable by the model, enabling accurate detection despite the original distortion while maintaining single-image computational efficiency.
Data Source
AI summary
System, methods, and other embodiments detecting and localizing objects within an image in a wide-view format using a synthetic representation. The method includes converting a real image in a wide-view format to a synthetic representation using a style model, wherein the synthetic representation depicts a distorted view of an object. The method also includes identifying features of the object using an extraction model that distinguishes different scales of the synthetic representation and a simulated scene to define structures associated with the distorted view. The method also includes detecting the object using a decoder model that identifies an attribute and a bounding box of the object from the features. The method also includes executing a task using the attribute and the bounding box to localize the object in the simulated scene.