Video Location Recognition via Visual-Audio Feature Integration
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Solution Overview
Problem
Current computer systems are ineffective in recognizing environments or locations from images due to their inability to interpret the content of images, as they treat a color image as a 3D array of numbers without contextual understanding, leading to poor results in image search and identification.
Innovation Solution
A method and system that utilizes video streams to identify environments or locations by extracting sequential frames, generating a database associating location identification with image details, and comparing image details to a database using deep learning algorithms, including private and public APIs, to accurately identify locations even from unique angles or without showing the primary object.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If a computer uses raw pixel values as independent features for image classification, then the image can be processed as a three-dimensional array of numbers, but the computer cannot recognize the content of the image leading to poor location identification results
Solution Approach 1:
The patent segments the image processing task into multiple stages: first extracting visual features from raw pixels, then incorporating audio features, and finally integrating both to identify location. This segmentation allows the system to process images in a structured way that leads to accurate location identification, overcoming the limitation of treating pixels as independent features.
Solution Approach 2:
The patent merges visual features extracted from image frames with audio features from the corresponding video audio stream. By combining these different types of features and training a machine learning model on their integration, the system achieves accurate location identification that neither modality could achieve alone, resolving the contradiction between processing capability and identification accuracy.
2Reliability
If the system processes all frames in a video sequence, then complete coverage of the environment is achieved, but the processing time and computational resources increase significantly
Solution Approach 1:
The patent applies partial action by extracting visual features from a selected subset of frames rather than processing every frame in the video sequence. This selective sampling maintains sufficient environmental coverage for reliable location identification while significantly reducing processing time and computational resource requirements.
Solution Approach 2:
The system performs preliminary action by pre-extracting and storing visual features from video frames in advance. These pre-processed features are then readily available for integration with audio features during location identification, eliminating the need for time-consuming real-time processing and reducing overall system response time.
3Productivity
If the system uses only visual features from images, then the processing is simpler and faster, but the location identification accuracy is insufficient
Solution Approach 1:
The patent merges visual features extracted from image frames with audio features from the corresponding video audio stream. By combining these different types of features and training a machine learning model on their integration, the system achieves accurate location identification that neither modality could achieve alone, resolving the contradiction between processing capability and identification accuracy.
Solution Approach 2:
The patent introduces an intermediary machine learning model that learns to integrate visual and audio features. This intermediary component processes both types of features and produces the final location identification, allowing the system to maintain high processing speed while achieving superior accuracy through the learned integration of multiple feature types.
4Adaptability or versatility
If the system trains on a large dataset with diverse viewpoints and angles, then the location recognition works from different perspectives, but the data collection and processing complexity increases
Solution Approach 1:
The patent applies dynamics by using video sequences that naturally capture environments from multiple viewpoints and angles as the camera moves. Rather than requiring static images from predetermined angles, the dynamic video data automatically provides diverse perspectives, enabling the system to learn robust location recognition across different viewpoints without increasing system complexity.
Solution Approach 2:
The patent uses video frames as copies of the actual environment captured at different moments and angles. By processing these visual copies along with audio information, the system learns to recognize locations from various perspectives. This copying approach provides diverse training data without requiring physically complex data collection systems.
Data Source
AI summary
The system and method of the present invention identify an environment or a location out of a sequence of information received in a video format. The invention provides a learning system and therefore the more videos that are received, relating to a specific environment/location, the more accurate the identification will be when a different image is later analyzed, including the ability to identify the environment/location seen from different viewpoint and angles.