Unified Body Key Point Detection Model for Real-Time Processing
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Solution Overview
Problem
Current body detection methods in computer vision are complex and require significant computational resources, making real-time processing challenging due to the intricate process of extracting body frames and key points from images.
Innovation Solution
A method and apparatus utilizing a body key point detection model comprising a feature extraction network, a body detection network, and a key point detection network, which imports images into a pre-established model to obtain body frames, key point positions, and judgment results, simplifying the detection process by characterizing corresponding relationships between images and detection outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional body detection methods are used (extracting minimum frame then performing key point recognition), then detection accuracy can be maintained, but the process complexity increases and processing speed decreases
Solution Approach 1:
The patent merges the body detection task and key point detection task into a single unified neural network model. The network simultaneously outputs both the body frame coordinates and key point coordinates in one forward propagation process, eliminating the need for separate extraction and processing steps. This integration maintains detection accuracy while significantly simplifying the overall process complexity.
Solution Approach 2:
The unified neural network model performs multiple functions: it detects body frames, identifies key points, and determines whether key points belong to the body all within a single model structure. This multi-functional approach replaces the traditional multi-step pipeline, reducing process complexity while maintaining comprehensive detection capabilities.
2Measurement precision
If traditional body detection methods are used (extracting minimum frame then performing key point recognition), then comprehensive body analysis can be achieved, but computational resources increase
Solution Approach 1:
By combining body detection and key point detection into a single neural network forward propagation, the patent eliminates redundant computational steps. The shared feature extraction layers are computed once and reused for both detection tasks, significantly reducing the total computational resources required compared to running separate detection pipelines.
Solution Approach 2:
The neural network performs preliminary feature extraction that serves both body detection and key point detection purposes. By computing common features once and reusing them for multiple detection objectives, the system avoids redundant calculations and reduces overall computational resource consumption.
3Loss of information
If traditional body detection methods are used (extracting minimum frame then performing key point recognition), then detailed body key point information can be obtained, but processing time increases
Solution Approach 1:
The unified neural network model simultaneously predicts body frame coordinates and key point coordinates in a single forward propagation pass. This parallel prediction approach maintains complete key point information while reducing processing time by eliminating sequential processing steps and intermediate frame extraction operations.
Data Source
AI summary
Embodiments of the present disclosure disclose a method and apparatus for detecting a body. A specific embodiment of the method includes: acquiring a to-be-detected image, the to-be-detected image including a body image; importing the to-be-detected image into a pre-established body key point detection model to obtain a body frame of a body corresponding to the body image, a position of a body key point, and a judgment result of whether the body key point belongs to the body in the to-be-detected image; and outputting the body frame of the body corresponding to the body image, the position of the body key point, and the judgment result of whether the body key point belongs to the body in the to-be-detected image.