Camera Identification via Command Response Analysis
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Solution Overview
Problem
Conventional video production systems face challenges in identifying camera models and characteristics, leading to potential corruption or loss of video streams when devices with unknown characteristics are used, as they lack the necessary information for improved video processing and device control.
Innovation Solution
A system and method where a video processing device sends commands to a camera to capture video samples, evaluates the responses to narrow down potential camera models, and associates the identified model with a network identifier for future reference, using a process that continues until confirmed command and response combinations match a specific camera model.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional video production systems are used without camera identification, then the system is simpler to operate, but video stream corruption or loss occurs when devices with unknown characteristics are used
Solution Approach 1:
The system performs camera identification and characteristic detection before video processing begins. The controller sends test commands to the camera device to detect its model and characteristics in advance, storing this information for subsequent video processing operations, thereby preventing corruption before it occurs
Solution Approach 2:
The system implements a feedback mechanism where the controller sends commands to the camera and evaluates the response video samples to determine camera characteristics. This closed-loop feedback process continues until the camera model is identified, ensuring reliable video processing through accurate camera identification
2Manufacturing precision
If camera identification processes are implemented, then video processing quality improves, but the time required for device setup and processing increases
Solution Approach 1:
The system uses a limited set of test commands targeting specific camera functions rather than exhaustive testing. By selecting only the most discriminating commands needed to identify camera model and characteristics, the system achieves sufficient identification accuracy while minimizing the time required for device setup
Solution Approach 2:
The system varies command parameters systematically to elicit different camera responses for identification. By changing command parameters and analyzing the resulting video samples, the system efficiently determines camera characteristics without requiring extensive testing time
3Measurement precision
If comprehensive camera characteristic detection is performed, then device control accuracy improves, but the number of commands and system complexity increases
Solution Approach 1:
The camera identification process is segmented into distinct stages: sending individual test commands, evaluating video samples, determining characteristics, and storing results. This segmentation allows the system to methodically detect camera properties through a structured sequence of simpler operations rather than a single complex process
Data Source
AI summary
Systems and processes are provided to identify a first camera model associated with a first video capture device of video capture devices within a system that aggregates video feeds from the video capture devices. A process includes receiving, by the video processing device, a first video sample from the first video capture device; sending, from the video processing device, a first command to the first video capture device; receiving, by the video processing device, a second video sample from the first video capture device subsequent to the sending of the first command; evaluating, by the video processing device, the first video sample and the second video sample in view of the first command to identify a first command response; and determining the first camera model based on the identified first command response.