Image Recognition Device Using Particle Filter Weighting
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Solution Overview
Problem
Existing image recognition technologies face challenges in maintaining robustness when tracking targets in varying environments, such as changes in lighting and crowd density, due to limitations in feature detection methods like HOG and color distribution features.
Innovation Solution
An image recognition device combines HOG and color distribution feature amounts using a particle filter technique, dynamically weighting these features to enhance robustness and adapt to changing conditions, allowing for effective tracking of targets in diverse environments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If a single feature detection method (HOG or color distribution) is used for image recognition, then the recognition process is simple, but the system cannot cope with changes in photographing state and tracking fails
Solution Approach 1:
The patent combines HOG feature amounts and color distribution feature amounts into a unified recognition framework. The particle filter integrates both feature types, allowing the system to leverage the strengths of each method (edge detection capability of HOG and color information of color distribution) to achieve robust tracking under varying photographing conditions
2Reliability
If multiple feature detection methods are combined to improve recognition robustness, then tracking reliability increases, but the device complexity increases
Solution Approach 1:
The patent introduces particle filters as an intermediary mechanism that seamlessly integrates HOG and color distribution feature amounts. This intermediary framework manages the complexity of combining multiple features by providing a unified probabilistic approach, where particles represent hypotheses about target position and are evaluated using both feature types, thus improving robustness without linearly increasing system complexity
Data Source
Figure 1(a)~1(b)
Figure 2(a)~2(d)
Figure 3(a)~3(c)
AI summary
To perform image recognition with high robustness. An image recognition device 2 sets an overall observation region 5 which surrounds a whole body of an object 3 and partial observation regions 6a to 6g which surround characteristic parts of the object 3 respectively to locations in an image 4 which are estimated to include captured images of the object 3. The image recognition device 2 clips images in the overall observation region 5 and the partial observation regions 6a to 6g, and calculates similarity degrees between them and previously learned images on the basis of a combination of two image feature amounts, i.e., an HOG feature amount and a color distribution feature amount. The image recognition device 2 calculates an optimum ratio (percentage) in combining the HOG feature amount and the color distribution feature amount individually for the overall observation region 5 and the partial observation regions 6a to 6g. This ratio is determined by setting a weight parameter αi for setting a weight used for combining the HOG feature amount and the color distribution feature amount to be included in a state vector and subjecting the result to complete search by a particle filter.