Digital Object Tracking with Position, Segment, and Affine Features
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Solution Overview
Problem
Existing systems for identifying and tracking digital objects across frames of digital content are prone to errors due to poor lighting and trajectory issues, leading to inconsistent object identification and tracking.
Innovation Solution
A digital object identification engine that utilizes position, segment, and affine object attributes to determine the similarity between detected objects in different frames, using a digital object manager to assign object IDs based on these attributes, thereby enhancing tracking accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing systems are used to identify and track digital objects across frames, then object identification can be performed, but tracking accuracy deteriorates due to poor lighting and trajectory issues
Solution Approach 1:
The patent segments object identification into multiple independent attribute evaluations: position attributes (center coordinates), segment attributes (pixel-based segmentation masks), and affine attributes (transformation matrices). Each attribute type handles specific tracking challenges independently, with segment attributes addressing lighting issues and affine attributes handling trajectory issues, then combines results through weighted similarity calculation.
Solution Approach 2:
The patent changes the parameter space from single-mode identification to multi-attribute parameter evaluation. Instead of relying on one identification method, it evaluates objects across multiple parameter dimensions (position, segment, affine) and uses weighted similarity scores to determine object identity, making the system robust to variations in lighting and trajectory conditions.
2Reliability
If simple object detection is used, then processing speed is maintained, but tracking reliability deteriorates under poor lighting and trajectory conditions
Solution Approach 1:
The patent merges three different attribute evaluation systems (position, segment, affine) into a unified tracking framework. Each attribute type provides complementary information, and their combined weighted similarity calculation produces more reliable tracking results than any single attribute could achieve alone, while maintaining manageable system complexity through modular architecture.
Solution Approach 2:
The patent creates a universal object identification engine that handles multiple types of visual variations (lighting changes, trajectory variations) through a single multi-attribute framework. The same engine processes all attribute types using consistent similarity calculation logic, making the system adaptable to various tracking scenarios without requiring separate specialized systems for each challenge.
Data Source
AI summary
Techniques for identifying and tracking objects in digital content are disclosed. In one embodiment, a method is disclosed comprising obtaining a frame of digital content, the frame comprising pixel data, detecting an object using the pixel data, determining a set of attributes for the detected object, the set of attributes comprising position, object segment and affine attributes, determining a similarity measurement for the detected object and a second object using the set of attributes corresponding to the detected object and the second object's set of attributes, and using the similarity measurement to make a similarity determination whether or not the detected object and the second object are a same object.