Video Content Aggregation Using Training Model Filtering
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Solution Overview
Problem
The challenge lies in efficiently aggregating and reducing the volume of video content from multiple surveillance cameras in smart home systems, as users face a cumbersome task in reviewing extensive footage, with existing systems failing to effectively filter out ordinary events, leading to information overload.
Innovation Solution
A method and system that utilize a training model to categorize and filter video content, applying image processing techniques and user-generated input to discard non-essential footage, allowing users to review only significant events, with the ability to adjust and refine the training model based on user feedback and characteristics of the premises.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If video content from multiple cameras is aggregated and presented to users, then complete surveillance coverage is achieved, but users face information overload and cumbersome review tasks
Solution Approach 1:
The system extracts and separates significant events from the aggregated video content using training models. Ordinary events are filtered out and removed from the presentation to users, while only significant events are retained and displayed. This extraction process resolves the contradiction by maintaining complete surveillance coverage through aggregation while eliminating information overload in user presentation.
2Reliability
If all video content is presented to users for review, then no important events are missed, but users spend excessive time reviewing footage
Solution Approach 1:
The system performs preliminary classification of video content using training models before presenting it to users. The training models pre-identify and categorize significant events, filtering out ordinary events in advance. This preliminary action ensures that no important events are missed while dramatically reducing the time users need to spend reviewing footage, as only pre-filtered significant events are presented.
3Reliability
If existing systems present all video footage to users, then complete monitoring is maintained, but user experience deteriorates due to information overload
Solution Approach 1:
The system uses feedback from user interactions with significant events to continuously refine and improve the training models. When users review or interact with presented events, this feedback is used to adjust the training models, making them more accurate at identifying significant events. This feedback loop maintains monitoring completeness while progressively improving user experience by reducing information overload through more precise event filtering.
Data Source
AI summary
Aspects of the subject disclosure may include, for example, systems and methods aggregating video content and adjusting the aggregate video content according to a training model. The adjusted aggregate video content comprises a first subset of the images and does not comprise a second subset of the images. The first subset of the images is determined by the training model based on a plurality of categories corresponding to a plurality of events. The illustrative embodiments also include presenting the adjusted aggregate video content and receiving identifications for the first subset of the images in the aggregate video content. Further, the illustrative embodiments include adjusting the training model according to the identifications and providing the adjusted training model to a network device. Other embodiments are disclosed.