Virtual Camera Analytics Using Cloud Containers for Remote Testing
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Solution Overview
Problem
Existing video capture devices are often inaccessible, making it difficult to test software updates and extract analytics from captured videos without direct access to the devices.
Innovation Solution
A virtual camera system is implemented using cloud-based compute devices to encode, stream, and generate analytics from video files without requiring direct access to the original capture devices, utilizing software containers to replicate camera functionalities and perform computer vision tasks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If video analytics are extracted using physical camera devices, then analytics can be generated from captured videos, but the video compute devices are inaccessible and cannot be accessed for testing software updates
Solution Approach 1:
The patent creates virtual camera instances that replicate the functionality of physical cameras through software containers. These virtual copies enable analytics processing and software testing without requiring physical device access, as the virtual instances mimic the exact behavior and output of the original cameras.
Solution Approach 2:
The patent introduces a virtualization layer as an intermediary between the physical camera and the analytics processing system. This layer uses software containers to mediate the connection, allowing remote access and testing while the physical device remains inaccessible.
2Productivity
If physical video compute devices are used to generate analytics, then analytics can be extracted from videos, but rapid provisioning and testing cannot be performed without hardware limitations
Solution Approach 1:
The system creates multiple virtual camera instances from software containers, allowing rapid provisioning of numerous analytics processing units without physical hardware. Each virtual instance can be deployed quickly through software instantiation, enabling high-volume analytics generation and parallel testing scenarios.
Solution Approach 2:
The patent implements dynamic virtual camera instantiation where virtual instances can be created, configured, and destroyed on-demand through software. This dynamic approach allows the system to scale analytics processing capacity flexibly based on workload requirements without permanent hardware commitments.
3Ease of operation
If virtual camera systems are implemented using cloud-based compute devices, then accessibility and scalability are improved, but software container execution and virtualization overhead are introduced
Solution Approach 1:
The patent uses standardized software containers that can execute the same analytics processing logic across different cloud environments and virtual camera instances. This universal container approach simplifies deployment and management by providing a consistent execution environment that works uniformly across the distributed cloud infrastructure.
Data Source
AI summary
A non-transitory, processor-readable medium stores instructions that, when executed by a processor, cause the processor to generate, using a first compute device, a video file. The non-transitory, processor-readable medium also stores instructions that, when executed by a processor, cause the processor to cause the video file to be sent to a second compute device that is cloud based, to cause the second compute device to: encode the video file, using a set of at least one software container, to generate an encoded video file; execute, at the set of at least one software container, a streaming protocol to generate a stream based on the encoded video file; and cause, via the set of at least one software container, generation of a set of analytics associated with the video file, based on the stream and using a virtualized software application environment.