Biological Object Tracking with Growth-Stage Probability Matching
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Solution Overview
Problem
Existing object tracking methods struggle to accurately track biological objects that undergo significant visual changes over extended periods, such as fruits, nuts, or flowers, across multiple images captured days or weeks apart.
Innovation Solution
Implementing a method using probability distribution classifiers (PDC) and predictors (PDP) machine learning models, such as CNNs and RNNs, to determine and predict probability distributions of biological objects across multiple classes, allowing for matching objects despite changing appearances.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional object tracking methods are used, then tracking of static objects is effective, but tracking of biological objects with changing appearances fails
Solution Approach 1:
The patent transforms object appearance data into probability distribution vectors that capture growth stages and visual characteristics. These parameter representations evolve with the biological object's development, allowing reliable tracking despite appearance changes by comparing probability distributions across time points rather than raw pixel data.
Solution Approach 2:
The patent introduces probability distribution vectors as an intermediary representation between raw image data and tracking decisions. These vectors serve as a mediator that abstracts away appearance variations while preserving identity information, enabling matching of biological objects across temporally-disparate images.
2Duration of action of stationary object
If images are captured days or weeks apart to monitor growth, then long-term tracking is enabled, but appearance changes make matching difficult
Solution Approach 1:
The patent pre-computes probability distribution vectors for each detected biological object in each image frame. These pre-computed representations capture the object's growth stage and visual characteristics at each time point, enabling accurate matching across long time spans by comparing these prepared vectors rather than processing raw images directly.
Solution Approach 2:
The patent replaces traditional image-based matching mechanisms with a probability distribution-based comparison system. Instead of comparing pixel intensities or feature extracts that are sensitive to appearance changes, the system compares probability vectors that are robust to growth and appearance variations, maintaining precision over extended tracking durations.
3Adaptability or versatility
If probability distribution classifiers and predictors are used, then tracking of changing appearances is achieved, but computational complexity increases
Solution Approach 1:
The patent segments the complex tracking problem into two distinct components: a Probability Distribution Classifier (PDC) that processes individual images to generate object representations, and a Probability Distribution Predictor (PDP) that performs temporal matching. This segmentation allows each component to be optimized independently and simplifies the overall system architecture despite the sophisticated probability-based approach.
Data Source
AI summary
Implementations are described herein for tracking objects with changing appearances across temporally-disparate images. In various implementations, a first probability distribution over a plurality of classes may be determined for a first biological object depicted in a first image captured at a first point in time. The classes may represent stages of growth of biological objects. Additional probability distribution(s) over the plurality of classes may be determined for candidate biological object(s) depicted in a second image captured at a second point in time subsequent to the first point in time. The candidate biological object(s) may potentially match the first biological object depicted in the first image. Based on a time interval between the first and second points in time, the first probability distribution may be compared to the probability distribution(s) of the candidate biological object(s) depicted in the second image to match one of the candidate biological object(s) depicted in the second image to the first biological object depicted in the first image.