Virtual Bounding Box Object Tracking with Scale Adaptation
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Solution Overview
Problem
Current object tracking algorithms in videos face challenges in accurately tracking objects with scale adaptation, particularly when objects change size, posture, or are rotated, due to limitations in handling complex motion and background interference, often requiring significant user input and struggling with noise and illumination changes.
Innovation Solution
The method involves filtering video frames to delineate foreground and background, constructing virtual bounding boxes to track objects based on contour and color, and using a Mean Shift tracker with extended bounding boxes to accommodate scale changes and background interference, while minimizing user input through intelligent background selection and propagation of foreground and background information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional object tracking algorithms are used, then tracking can be performed with simple implementation, but tracking accuracy deteriorates when objects undergo scale changes, rotation, or complex motion
Solution Approach 1:
The patent segments the object tracking problem into multiple components: contour detection, color segmentation, and shape matching. By dividing the tracking task into these distinct segments, the system achieves higher accuracy in handling scale changes and rotation while maintaining manageable computational complexity through modular processing steps.
Solution Approach 2:
The patent extends traditional 2D bounding box tracking by incorporating shape contours and color information as additional dimensions. This multi-dimensional approach allows the tracker to accurately follow objects undergoing scale changes and rotation by matching contours and colors across frames rather than relying solely on position and size.
2Reliability
If extensive filtering and processing is applied to handle noise and background interference, then tracking reliability improves, but computational complexity and processing time increase
Solution Approach 1:
The patent performs preliminary filtering by detecting object contours and establishing color segments in early frames before full tracking begins. This preliminary action creates a reference model that simplifies subsequent tracking operations, reducing the need for extensive real-time filtering while maintaining high reliability in noisy conditions.
Solution Approach 2:
The patent creates a copy of the object's contour and color characteristics from reference frames and uses this copied model for comparison in subsequent frames. This copying approach enables robust tracking through noise and background interference by matching the copied model against current frame features without requiring complex real-time filtering.
3Ease of operation
If minimal user input is required, then ease of operation improves, but the system's ability to accurately identify and track the correct object deteriorates
Solution Approach 1:
The patent implements self-service by enabling the system to automatically identify and track objects through intelligent contour detection and color segmentation without requiring user input. The system autonomously establishes tracking parameters and maintains object identification accuracy through automated shape and color matching across frames.
Solution Approach 2:
The patent automatically adjusts tracking parameters such as contour threshold, color tolerance, and bounding box expansion based on scene analysis. These parameter changes enable the system to maintain high object identification accuracy with minimal user input by adapting to different tracking conditions automatically.
4Adaptability or versatility
If bounding boxes are extended to accommodate scale changes, then adaptability improves, but false detection of background objects increases
Solution Approach 1:
The patent applies local quality by using contour and color information specifically within the extended bounding box region rather than treating the entire box uniformly. This localized approach allows the system to accommodate scale changes by expanding the search area while maintaining accurate object identification through contour and color matching in the specific region of interest, reducing false detection of background objects.
Solution Approach 2:
The patent implements dynamic bounding box adjustment by expanding the box size adaptively based on detected object scale changes while maintaining contour and color constraints. This dynamic approach allows the bounding box to grow when objects enlarge but prevents false detection by continuously matching contour and color features, ensuring the expanded region still corresponds to the actual object.
Data Source
AI summary
A computer-implemented method for tracking an object in a video including, in a first video frame: filtering the first video frame to delineate foreground from background and to select from the foreground an object to be tracked, said object identified at least by contour, center and color; constructing a first virtual bounding box minimally enclosing said object; using a tracking algorithm to establish a target location for the object in the second video frame based on similarity to the object identified in the first video frame; constructing a second virtual bounding box minimally enclosing the target shape at the target location; extending the second virtual bounding box; filtering the second video frame to delineate foreground from background using weighted values propagated from at least the first frame; scrutinizing the output of the second video frame by matching at least shape and color of the target shape at the target location.