Computer Vision Framework for Live Video Object Tracking
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Solution Overview
Problem
Conventional systems for real-time object identification in live video streams, particularly in dynamic and noisy environments, face challenges due to high computational requirements, limited processing power, and inefficiencies in computer vision processing, leading to inaccurate results and resource exhaustion in devices like smartphones.
Innovation Solution
An improved image processing framework that subdivides the processing task into detection, classification, and matching sub-tasks, reduces the number of object categories to recognize, eliminates the need for manual annotation, and uses synthetic data generation for training, thereby reducing computational cycles and increasing accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional computer vision or machine learning technologies are used for real-time object tracking in live video streams, then object identification capability is improved, but processing power requirements increase significantly and limited device resources are exhausted
Solution Approach 1:
The patent segments the complex object identification task into distinct stages: detection of potential objects, classification of detected objects, and tracking of classified objects through frames. This segmentation allows each stage to use optimized algorithms appropriate to its specific function, reducing overall computational requirements while maintaining identification accuracy in live video streams
Solution Approach 2:
The patent performs preliminary detection and classification operations on video frames before final tracking and identification. By pre-processing frames to identify and classify objects of interest beforehand, the system reduces the computational burden during real-time tracking, enabling accurate object identification on devices with limited processing power
2Reliability
If conventional image processing techniques are used to detect and track objects in busy and dynamic environments, then object detection capability is improved, but processing efficiency decreases and manual post-processing is required
Solution Approach 1:
The patent implements automated detection, classification, and tracking algorithms that operate without manual post-processing intervention. The system self-corrects and refines object identification through continuous frame analysis and tracking, eliminating the need for manual processing while maintaining reliable detection accuracy in dynamic environments
Solution Approach 2:
The patent employs feedback mechanisms where tracking results from previous frames inform detection and classification in current frames. This iterative feedback loop allows the system to maintain high detection reliability while improving processing efficiency by using historical data to guide current frame analysis, reducing redundant computations
3Adaptability or versatility
If open-source computer vision libraries are used for object recognition, then functionality is improved, but system complexity increases and ease of use decreases
Solution Approach 1:
The patent merges detection, classification, and tracking functionalities into a unified integrated system. By combining these separate computer vision operations into a single cohesive framework with standardized interfaces, the system maintains versatile object recognition capabilities while reducing overall system complexity and improving ease of use for developers
4Loss of information
If vast storage is allocated for frame analysis in real-time, then analysis completeness is improved, but storage requirements increase and processing speed decreases
Solution Approach 1:
The patent extracts and processes only the essential features and data from video frames necessary for object identification and tracking. By extracting relevant information rather than storing and analyzing complete frames, the system maintains analysis completeness for detection purposes while significantly reducing storage requirements and accelerating real-time processing speed
Data Source
AI summary
Disclosed are systems and methods for improving interactions with and between computers in content searching, hosting and/or providing systems supported by or configured with devices, servers and/or platforms. The disclosed systems and methods provide an image processing framework that sub-divides computer vision techniques into three computationally efficient steps: detection, classification and matching. These steps provide an improved image processing framework that can analyze live stream data of a media file, in real-time, in order to identify and track specific digital objects depicted therein. This enables not only image processing detection results, but also the capabilities of augmenting the video stream with additional data related to the detected object.