Semantic Video Segmentation for Object Identification
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face difficulties in identifying and obtaining information about objects or products featured in digital video content, such as movies or TV shows, during consumption, as existing technologies lack efficient methods for object recognition and seamless integration with user queries.
Innovation Solution
The system employs automated object identification and mapping within video content using algorithms for object detection, facial recognition, and machine learning to generate product appearance timelines, allowing users to query and purchase products directly while watching content through voice or text inputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If automated object identification algorithms are implemented in video content, then object recognition accuracy is improved, but device complexity increases
Solution Approach 1:
The system segments video content into discrete frames and processes each frame independently through object detection algorithms. This segmentation allows complex video analysis to be broken down into manageable units, improving recognition accuracy while managing computational complexity through parallel processing of individual frames.
Solution Approach 2:
The system performs preliminary object detection and identification on video frames before user interaction occurs. By pre-processing video content to identify objects, the system prepares recognition data in advance, improving response accuracy when users query about objects while reducing real-time computational burden.
2Loss of time
If real-time object detection is performed during video consumption, then user query response time is improved, but processing speed decreases
Solution Approach 1:
The system performs object detection and creates object timelines in advance during video playback or pre-processing, so that when users submit queries about objects, the identification work is already completed. This preliminary action dramatically reduces query response time without impacting real-time video processing speed.
Solution Approach 2:
The system creates copies of object detection results and stores them in object timelines that can be quickly queried without re-processing the original video data. This copying approach allows rapid response to user queries while maintaining efficient video playback and processing speeds.
3Measurement precision
If comprehensive object tracking is implemented throughout video content, then object identification accuracy is improved, but computational resources increase
Solution Approach 1:
The system segments video content into frames and groups objects by scene or temporal proximity, processing only relevant segments for tracking. This selective segmentation reduces computational resources required for comprehensive tracking while maintaining identification accuracy through focused analysis of object-containing segments.
Solution Approach 2:
The system discards processed video frames after extracting object information and recovers computational resources for new processing tasks. By managing memory and computational resources through systematic discarding of processed data, the system maintains accurate object tracking while controlling resource consumption.
Data Source
AI summary
Systems, methods, and computer-readable media are disclosed for systems and methods for semantic video segmentation to identify objects appearing in video content. Example methods may include determining a voice-based request at a first device to identify an object appearing in video content, determining a first video identifier for video content being consumed in association with the first device, determining a timestamp associated with the video content at a time the voice-based request was received, determining, using one or more knowledge graphs, a set of product identifiers for products present in the video content at a scene corresponding to the timestamp, and generating a response to the voice-based request comprising a first product identifier in the set of product identifiers.