Multi-Camera Vantage Point Transformation for Unified Bird's-Eye Surveillance
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Solution Overview
Problem
Current video surveillance systems lack the ability to combine video feeds from multiple cameras effectively, making it difficult to provide a unified, controllable 'birds eye' view of a surveillance area, especially when camera vantage points and fields of view differ significantly.
Innovation Solution
A method is introduced that involves receiving video streams from multiple cameras, applying a vantage point transformation to align views from different camera perspectives to a common vantage point, and rendering these transformed views on a display, allowing for stitching together of video frames with overlapping fields of view to create a comprehensive, top-view image.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If video feeds from multiple cameras with different vantage points and fields of view are combined, then a unified birds eye view is achieved, but the system complexity increases
Solution Approach 1:
The patent introduces a virtual scene as an intermediary representation that mediates between multiple camera views. The virtual scene acts as a common coordinate system where video frames from different cameras are transformed and integrated, enabling the unified birds-eye view without direct complex manipulation of all camera feeds simultaneously
Solution Approach 2:
The patent segments the video processing task into distinct modules: individual camera view processing, transformation to virtual scene coordinates, and composite rendering. This segmentation allows each module to be optimized independently and processed in parallel, reducing overall system complexity
2Speed
If video streams from multiple cameras are processed and transformed in real-time, then dynamic birds eye view is achieved, but computational load increases
Solution Approach 1:
The patent performs preliminary actions by pre-calculating transformation parameters and establishing the virtual scene coordinate system before real-time processing. Camera calibration data and transformation matrices are computed in advance, reducing the computational burden during real-time video stream processing
Solution Approach 2:
The patent implements dynamic processing by adjusting the level of detail and processing intensity based on the current view requirements. When the virtual camera is zoomed out, lower resolution processing is used; when zoomed in, higher resolution processing is applied only to relevant regions, optimizing computational resource usage
3Area of stationary object
If overlapping fields of view from multiple cameras are stitched together, then coverage area is improved, but alignment precision becomes more difficult
Solution Approach 1:
The patent replaces manual or mechanical alignment methods with automated image processing techniques. Feature detection algorithms automatically identify corresponding points in overlapping fields of view, and computational geometry methods calculate the optimal transformation parameters for stitching, achieving high precision without mechanical intervention
Data Source
AI summary
A method for monitoring a surveillance area includes receiving a plurality of video streams that are captured by a plurality of video cameras in the surveillance area, each of the plurality of video streams capturing one or more objects in the surveillance area that are within a camera field of view (FOV) of the corresponding video camera and from a camera vantage point of the corresponding video camera. A vantage point transformation is applied to each of two or more objects captured in two or more of the plurality of video streams, the vantage point transformation transforming a view of each of the two or more objects from the camera vantage point of the corresponding video camera to a common vantage point. The transformed view of each of the two or more objects from the common vantage point is rendered on a display.