Automated Camera Predictive Framing for Dynamic Events
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Solution Overview
Problem
Conventional automated image capture systems perform poorly in rapidly changing scenes due to latency in adjusting camera orientation and zoom settings, often missing the critical moment of events like a winning athlete crossing the finishing line.
Innovation Solution
An automated system using a pan/tilt base and a zoom-adjustable camera predicts the future location and timing of objects in a scene, determines the best framing subset based on predicted object locations and event scores, and adjusts the camera orientation and zoom to capture high-quality images of events involving multiple moving objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If an automated system adjusts camera orientation and zoom settings in real-time, then the system can capture images of moving objects, but the latency in adjusting settings causes the system to miss critical moments in rapidly changing scenes
Solution Approach 1:
The system performs preliminary actions by predicting future object locations and pre-adjusting camera orientation and zoom settings before the critical moment occurs. This anticipatory approach eliminates the latency problem by having the camera ready to capture the exact moment the event happens, rather than reacting after detection.
Solution Approach 2:
The system dynamically adapts camera parameters based on predicted object trajectories and event probabilities. By continuously updating predictions and adjusting settings in advance, the system maintains optimal capture conditions for rapidly changing scenes, resolving the contradiction between real-time adjustment and timing accuracy.
2Manufacturing precision
If a narrow field of view is used to maximize object size and resolution, then image quality improves, but the ability to include multiple desired objects in the frame is reduced
Solution Approach 1:
The system dynamically adjusts the field of view based on predicted object locations and the importance of different objects. By using prediction, the camera can switch between narrow and wide fields of view in advance, ensuring high resolution for critical objects while maintaining the ability to include multiple objects when needed, thus resolving the contradiction between resolution and adaptability.
Data Source
AI summary
A method of capturing an image of a scene. A current location of a plurality of objects in a frame of a video capturing the scene having one or more events of interest, is determined. For at least one of the events of interest, a time and a location for each of the plurality of objects associated with the event of interest is predicted based on the current location of the plurality of objects. A frame subset score is determined for each of a plurality of frame subsets in the frame, each of the plurality of frame subsets including one or more of the plurality of objects based on the predicted time and the predicted location for the event of interest. One of the determined plurality of frame subsets is selected based on the determined frame subset score. An image of the event of interest is captured using a camera, based on a camera orientation setting for the selected frame subset, where the captured image comprises the selected frame subset.