Video Understanding Platform Single Decode Parallel Classification
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Solution Overview
Problem
Conventional audio/video classification systems face challenges in real-time classification due to significant processing overhead from multiple decode operations and inability to gracefully update classifiers, leading to operational delays and inefficiencies as the volume of content increases.
Innovation Solution
A video understanding platform that identifies data format requirements for machine-learning-based classifiers, decodes audio/video streams once, creates new instances of classifiers, transforms data to meet format requirements, and performs parallel classification, allowing for independent updates and efficient scaling.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If separate decode operations are performed for each discrete classifier, then each classifier can process video data in its specific format, but processing overhead increases significantly and real-time classification becomes impossible
Solution Approach 1:
The system segments the processing pipeline into distinct components: a single decode operation that produces base video data, followed by multiple transform operations that convert the data into different formats required by each classifier. This segmentation allows the decode operation to be performed only once while still satisfying the specific format requirements of each classifier through subsequent transforms.
Solution Approach 2:
The system merges multiple decode operations into a single decode operation followed by multiple transform operations. Instead of decoding the video stream separately for each classifier, the system performs one decode to produce base video data, then applies multiple transforms to generate the required format variations, thereby combining what would have been separate processing chains into a unified efficient pipeline.
2Adaptability or versatility
If classifiers are updated in conventional systems, then newer classifiers can be deployed, but all current classification operations must finish or new operations must pause, resulting in operational delays
Solution Approach 1:
The system implements dynamic classifier management where classifiers can be updated, added, or removed without disrupting ongoing classification operations. The architecture allows the classifier set to be modified while classification operations continue with the previously configured classifiers, enabling smooth transitions and updates without system downtime or operational delays.
3Reliability
If multiple decode operations are performed for each classifier, then each classifier receives properly formatted data, but processing overhead makes real-time classification a near impossibility
Solution Approach 1:
The system performs the decode operation as a preliminary action that produces base video data before the classification operations begin. This pre-processing step prepares the data in a way that can be efficiently transformed into the various formats needed by different classifiers, eliminating the need to repeat the decode operation for each classifier and enabling real-time processing.
Data Source
AI summary
The disclosed computer-implemented method may include identifying data format requirements for one or more machine-learning-based audio/video classifiers. The classifiers may be configured to detect classifiable features of decoded audio/video data. The method may also include decoding once, for the one or more classifiers, a video stream into audio/video data based on the identified data format requirements, and creating a new instance of each of the one or more classifiers. In addition, the method may include transforming the audio/video data for each instance based on the respective data format requirements and providing the respective transformed audio/video data to each instance. The method may also include performing, in parallel, classification of each transformed audio/video data by each respective instance and then terminating each instance of the one or more classifiers. Various other methods, systems, and computer-readable media are also disclosed.