3D Object Localization Using Vanishing Point Line Sampling
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Solution Overview
Problem
Conventional multi-camera approaches for 3D object localization are not suitable for real-time applications due to the high computational cost of coordinate transformations required for pixel-based processing in multiple camera views.
Innovation Solution
A vanishing point-based line sampling method that generates 2D line samples from pre-calibrated vanishing points in each camera view, constructs 3D line samples, and determines 3D object locations by clustering these samples, significantly reducing the computation of coordinate transformations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pixel-based processing with coordinate transformations is applied in conventional multi-camera approaches, then 3D object localization accuracy is improved, but computational cost increases making it unsuitable for real-time applications
Solution Approach 1:
The patent segments the continuous pixel-based coordinate transformation process into discrete line sampling operations. Instead of transforming all foreground pixels, the method extracts line samples from vanishing points and transforms only these sampled lines, dramatically reducing computational load while preserving localization accuracy through strategic sampling of critical geometric features
Solution Approach 2:
The patent applies partial action by processing only a subset of image data (line samples from vanishing points) rather than all foreground pixels. This selective sampling approach transforms minimal necessary data to achieve 3D localization, reducing computation from O(N) pixels to O(M) lines where M << N, enabling real-time performance
2Reliability
If all foreground pixels are transformed to 3D reference planes in multiple camera views, then comprehensive object localization is achieved, but computation time increases significantly
Solution Approach 1:
The patent performs preliminary action by pre-calibrating vanishing points from camera parameters before processing video frames. These pre-computed vanishing points serve as fixed reference points for line sampling, eliminating the need for repeated calibration computations and enabling rapid 3D localization in real-time applications
Solution Approach 2:
The patent extracts only the essential geometric information (line samples from vanishing points) needed for 3D localization, discarding redundant pixel data. This extraction approach transforms minimal critical data from each camera view, achieving complete object localization without processing all foreground pixels, thus reducing computation time significantly
Data Source
AI summary
An efficient 3D object localization method using multiple cameras is provided. The proposed method comprises a three-dimensional object localization process that firstly generates a plurality of two-dimensional line samples originated from a pre-calibrated vanishing point in each camera view for representing foreground video objects, secondly constructs a plurality of three-dimensional line samples from the two-dimensional line samples in all the multiple camera views, and thirdly determines three-dimensional object locations by clustering the three-dimensional line samples into object groups.