Video Object In-Painting with Dynamic Multi-Model Tracking
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Solution Overview
Problem
Conventional object in-painting techniques in video streams suffer from poor performance in complex situations, leading to issues such as frequent object ID switching and lost tracks due to challenges like occlusions and variable scene illumination.
Innovation Solution
A dynamic fusing approach is employed, tailoring the use of multiple object tracking models based on their performance metrics, combining them using a weighting scheme that adjusts model importance dynamically depending on the context, such as similarity and crowdedness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional object in-painting techniques are used, then the video stream can be edited to remove or alter objects, but the tracking precision deteriorates due to frequent object ID switching and lost tracks in complex situations
Solution Approach 1:
The system dynamically adjusts tracking parameters and model weights based on scene complexity, occlusion levels, and illumination conditions. The object tracking model transitions between different operational states to maintain precision across varying video conditions, preventing ID switching and track loss.
Solution Approach 2:
The patent employs a composite tracking approach that fuses multiple object tracking models with different strengths. By combining models specialized in handling occlusions, illumination changes, and motion patterns, the system achieves more reliable tracking than any single model could provide alone.
2Measurement precision
If multiple object tracking models are applied to improve tracking precision, then the device complexity increases due to the need to manage and fuse multiple models
Solution Approach 1:
The system introduces a fusion layer that acts as an intermediary between multiple tracking models and the final tracking output. This fusion mechanism consolidates predictions from multiple models using weighted combinations, managing complexity by providing a unified interface while leveraging the strengths of individual models.
Solution Approach 2:
The object tracking system is designed with multi-functional capabilities that allow a single unified framework to handle multiple tracking scenarios. The system can adaptively select and combine different tracking strategies within one architecture, reducing the need for separate specialized systems for different object types or conditions.
Data Source
AI summary
One or more objects in a video stream may be selectively in-painted. In-painting refers to the replacement of a portion of a frame or frames in a video stream with updated image data. In-painting may help to protect privacy by replacing an image of a person, document, password, or other sensitive imagery. Multiple object tracking may be used to track objects across different image frames as well as to determine and persist object identity information across the different image frames. In addition, a video stream may be analyzed to identify activities being performed in the video stream. Then, objects may be in-painted depending on factors such as the identity of the object (e.g., a particular person) and/or the activity or activities being performed. For example, a known individual performing a permitted activity may be in-painted, while an unknown individual performing a prohibited activity may not be in-painted.