Machine Learning Detectors for Real-Time Video Object Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional search engines are limited in their ability to accurately identify objects within video content using text inputs, as text cannot accurately describe video content, and they struggle to process the vast amount of digital media created and shared online, making it difficult to find desired content in real-time.
Innovation Solution
The system employs machine-learning detectors, such as convolutional neural networks, to identify objects in media content items by analyzing visual features, allowing users to search for specific objects, faces, or actions within videos or frames, and provides user interfaces for reviewing and retraining detectors based on search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If text-based search engines are used to search video content, then the search process is simple and fast, but the accuracy of identifying objects within video content is poor
Solution Approach 1:
The patent introduces machine learning detectors as an intermediary component between the user search query and the video content. These detectors are trained to recognize specific objects, faces, or actions in video frames, acting as a mediator that translates text-based search intent into visual content identification. This resolves the contradiction by enabling accurate object identification without requiring the search engine itself to become overly complex.
Solution Approach 2:
The system performs preliminary training of machine learning detectors using labeled video data before actual search operations. This preliminary action creates pre-trained models that can quickly and accurately identify objects during runtime searches, eliminating the need for complex real-time analysis and improving both accuracy and efficiency.
2Productivity
If manual review of video files is performed by curators, then the accuracy of content identification is high, but the processing speed and scalability are insufficient
Solution Approach 1:
The system implements self-service through automated machine learning detectors that independently analyze video content without human intervention. These detectors automatically identify objects, faces, and actions in real-time, providing both high processing speed and accurate identification. The system serves itself by using trained models to perform what would otherwise require manual curator review.
Solution Approach 2:
The patent replaces the mechanical process of manual video review with automated machine learning-based detection. Instead of human curators physically watching and analyzing videos, the system uses trained neural networks and computer vision algorithms to automatically identify content, dramatically increasing processing speed while maintaining or improving accuracy.
3Measurement precision
If machine-learning detectors are trained using extensive example media content items, then the detection accuracy improves, but the training time and computational resources increase
Solution Approach 1:
The system applies partial training strategies where detectors are trained on carefully selected subsets of example media content that are most representative of the target objects. Rather than exhaustively training on all possible variations, the system uses curated training sets that provide sufficient accuracy while minimizing training time and computational resources.
Data Source
AI summary
Described herein are systems and methods that search videos and other media content to identify items, objects, faces, or other entities within the media content. Detectors identify objects within media content by, for instance, detecting a predetermined set of visual features corresponding to the objects. Detectors configured to identify an object can be trained using a machine learned model (e.g., a convolutional neural network) as applied to a set of example media content items that include the object. The systems and methods describe techniques that search videos and media content to determine the presence of unknown objects, generate novel detectors trained to identify the unknown objects, and apply the novel detectors to historical media content to identify previous appearances of the unknown objects.