Object Recognition Across Pose Changes Using Adjacent Frame Matching
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Solution Overview
Problem
Conventional object recognition technologies struggle with low accuracy when recognizing objects that undergo form changes, such as a cat transitioning from a standing to a lying position, often misidentifying them as different objects.
Innovation Solution
The method employs an object recognition algorithm that analyzes the similarity and correlation between adjacent frames of images, using pixel movement speed and direction to determine if objects are the same, leveraging a stored object matching template with feature information for accurate recognition across varying forms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional object recognition technology is used to recognize objects in fixed forms, then recognition accuracy for static objects is improved, but recognition accuracy for objects with changing forms deteriorates
Solution Approach 1:
The system pre-calculates and stores multiple pose templates for each object type before recognition occurs. When an object is detected, the system compares the detected object against these pre-prepared templates to determine both identity and pose, eliminating the need for real-time pose estimation and improving both speed and accuracy.
Solution Approach 2:
The system transitions from static single-pose recognition to dynamic multi-pose recognition by incorporating pose detection capabilities. The recognition system adapts to different object poses by selecting appropriate pre-calculated templates, enabling accurate recognition regardless of object orientation or configuration.
2Productivity
If object recognition is performed on each frame independently, then processing speed is improved, but recognition accuracy for moving objects deteriorates
Solution Approach 1:
The system uses detection results from previous frames as feedback to guide current frame detection. By tracking object positions and poses across frames and using this information to inform subsequent detections, the system maintains high processing speed while improving accuracy through temporal consistency and motion prediction.
Data Source
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AI summary
An object recognition method and a terminal device are provided. The method includes: A terminal device recognizes a first target object in a first frame of image. The terminal device recognizes a second target object in a second frame of image adjacent to the first frame of image. If a similarity between the first target object and the second target object is greater than a preset similarity, and a moving speed is less than a preset speed, the terminal device determines that the first target object and the second target object are a same object. In this method, the terminal device can recognize whether objects in adjacent frames of images are a same object, to help improve object recognition accuracy.