Supervised Localization Training for Robust Object Counting
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Solution Overview
Problem
Existing object counting methods struggle with variability in object shape, size, pose, and appearance, as well as occlusion and environmental factors, leading to inefficiencies in localization and counting tasks.
Innovation Solution
A method and system for training a machine learning localization model that involves generating heat maps, correcting initial probabilities using these maps, selecting object candidates based on corrected probabilities, and iteratively refining the model with a loss function to improve localization accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If detection-based methods are used to estimate object location and shape, then localization accuracy is improved, but object counting performance deteriorates
Solution Approach 1:
The patent combines detection-based localization methods with regression-based counting methods into a unified framework. The system uses detection models to generate object proposals with location and shape estimates, then applies regression techniques to count objects based on these proposals, achieving both accurate localization and counting simultaneously
Solution Approach 2:
The patent segments the object counting task into distinct stages: first generating object proposals through detection-based methods to achieve accurate localization, then applying regression-based counting on these segmented proposals. This segmentation allows each component to optimize for its specific function while contributing to the overall goal
2Measurement precision
If the model learns to detect objects with location and shape, then localization capability is improved, but counting accuracy deteriorates
Solution Approach 1:
The patent implements a dynamic training framework where the system adapts between detection and counting objectives during training. The loss function dynamically balances localization and counting objectives, allowing the model to learn both capabilities without one deteriorating the other
3Quantity of substance
If regression-based models are used for direct count prediction, then counting performance is improved, but localization capability deteriorates
Solution Approach 1:
The patent creates a universal model that performs both localization and counting functions. The system uses a multi-task learning approach where the same model architecture and training framework support both detection-based localization and regression-based counting, making the system multi-functional
Data Source
AI summary
A method for training a machine learning localization model to localize objects belonging to a given class within an image, the method comprising: receiving images each comprising objects of the given class; and for each image: receiving a heat map generated using the machine learning localization model; identifying proposals each corresponding to a potential object, each proposal having associated thereto an initial probability that the proposal corresponds to the potential object; for each proposal, correcting the initial probability using the heat map; selecting given ones of the proposals having a greatest corrected probability, thereby identifying object candidates; and calculating a loss for the machine learning localization model based on a location of the object candidates within the training image and the heat map; and providing the calculated loss to the machine learning localization model.