CNN Video Detection for Synchronized Adult Product Motion
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Solution Overview
Problem
Current adult sex products lack the ability to adjust their motion based on the image content and sexual motion in adult videos, resulting in a weak sense of interaction and affecting user experience.
Innovation Solution
A video detection method using a convolutional neural network (CNN) to extract and classify features from video frames, generating an auxiliary control signal to synchronize the motion of the adult sex product with the video content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a CNN model is used to perform feature extraction and classification on video frames to generate control signals, then the interaction and immersion of the adult sex product is enhanced, but the device complexity increases
Solution Approach 1:
The patent introduces an intermediary system consisting of the CNN model, feature extraction module, and control signal generation module that acts as a mediator between the video content and the adult sex product. This intermediary processes video frames, extracts features, classifies them, and generates appropriate control signals, thereby enabling intelligent interaction without directly modifying the core product structure.
Solution Approach 2:
The patent replaces traditional mechanical or simple electronic control systems with an intelligent vision-based system using convolutional neural networks. Instead of using complex mechanical sensors and actuators to detect and respond to video content, the system uses image processing and AI algorithms to analyze video frames and generate control signals, substituting mechanical complexity with computational intelligence.
2Measurement precision
If video frame analysis and classification are performed in real-time to control product motion, then the synchronization with video content is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent applies preliminary action by pre-training the CNN model with extensive video data before deployment. The model learns to recognize patterns, extract features, and classify video frames during the training phase, so that during real-time operation, the processing is significantly faster and more efficient. This pre-processing of knowledge reduces the computational burden during actual use.
Solution Approach 2:
The patent segments the video processing task into distinct stages: frame extraction, feature extraction, classification, and control signal generation. By dividing the complex processing task into smaller, specialized modules, the system can optimize each stage independently and process frames more efficiently in real-time.
Data Source
AI summary
The present disclosure provides a video detection method and an adult sex product. A target video image is inputted into a convolutional neural network (CNN) model for feature extraction to obtain a feature image of each frame. Extracted feature images are then classified. A system scores classified feature images, generates an auxiliary control signal based on a scoring result, and sends the auxiliary control signal to an external adult sex product to control its motion mode, thereby implementing synchronization or interaction between the video content and the device motion.