Video Object Identification via Content Processing Engine
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Solution Overview
Problem
Users face frustration and inefficiency in identifying and finding items featured in movies or TV shows due to the lengthy process of searching online retail websites, leading to increased traffic and latency issues for all users.
Innovation Solution
A content processing engine identifies objects within video content by processing video frames to detect scenes, extract object features, and match them with online retailer catalogs, providing metadata that allows for real-time identification and procurement of featured items during video playback.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually search for items on online retail websites, then they can potentially find the desired items, but the process is lengthy and frustrating
Solution Approach 1:
The system performs preliminary actions by automatically detecting and identifying items in video content before users need to search for them. The content processing engine extracts object features from video frames, compares them with retailer catalogs, and prepares identification results in advance, eliminating the need for users to manually search and significantly reducing search time while maintaining accurate item identification
Solution Approach 2:
The content processing engine acts as an intermediary between video content and online retailer catalogs. It automatically bridges the gap by detecting objects in videos, extracting their features, matching them with catalog items, and providing identification results to users, thereby eliminating the manual search process and reducing the time users spend searching for items
2Ease of operation
If users repeatedly search for items on online retailer websites, then they can find what they want, but this increases website traffic and causes latency for all users
Solution Approach 1:
The content processing engine serves as an intermediary that performs item identification offline before users need to access the website. By automatically detecting items in video content, extracting features, and matching with retailer catalogs in advance, the system provides users with ready-made identification results, eliminating repeated search traffic and reducing website latency for all users while maintaining ease of item finding
Solution Approach 2:
The system performs preliminary item identification and catalog matching before users initiate searches. The content processing engine processes video content, identifies objects, and prepares identification results in advance, so when users view the video, items are already identified and ready for procurement, eliminating the need for repeated website searches and reducing overall website traffic and latency
3Measurement precision
If a content processing engine processes video frames to identify objects, then item identification accuracy improves, but processing complexity increases
Solution Approach 1:
The content processing engine segments the complex task of item identification into distinct modular components: video frame processing, object detection, feature extraction, catalog matching, and result generation. Each module handles a specific aspect of the process, making the overall system more manageable and maintainable while achieving high object detection accuracy through specialized processing at each stage
Solution Approach 2:
The content processing engine is designed as a universal system that handles multiple functions within a single integrated platform. It can process different types of video content, detect various objects, extract diverse features, and match with retailer catalogs, thereby managing complexity through consolidation while maintaining high detection accuracy across different scenarios
Data Source
AI summary
Systems, methods, and computer storage media are described herein for identifying objects within video content. Utilizing an image recognition technique (e.g., a previously trained neural network) an object may be detected within a subset of the video frames of a scene of the video content. Metadata may be generated comprising a set of object attributes (e.g., an object image, a URL from which the item may be procured, etc.) associated with the object detected within the subset of video frames. A request for object identification may subsequently be received while a user is watching the video content. A run time of the request may be utilized to retrieve and display one or more object attributes (e.g., an object image with an embedded hyperlink) corresponding to an object appearing in the scene.