Neural Network Fine-Tuning for Real-Time Subject Tracking
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Solution Overview
Problem
Conventional machine learning-based tracking systems are limited by the need for extensive training data and are unable to adjust or retrain in real-time, making them inflexible and ineffective for tracking specific individuals in dynamic environments.
Innovation Solution
A deep learning-based tracking system that performs real-time training using initialization video data to generate Subject detection and tracking neural networks, allowing for dynamic reconfiguration and improved classification of specific individuals without labeled training data, utilizing pre-trained neural networks and onboard GPUs for efficient processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional machine learning systems are trained on large volumes of data, then tracking accuracy improves, but training time and computational power requirements increase significantly
Solution Approach 1:
The system performs preliminary training on large datasets before deployment, creating a pre-trained neural network model. This allows the system to have general object detection capabilities beforehand, eliminating the need for extensive retraining when deployed. The pre-training phase captures general patterns, while deployment focuses on specific subject adaptation.
Solution Approach 2:
The training process is segmented into two distinct phases: (1) general object detection training on large diverse datasets, and (2) subject-specific fine-tuning on smaller targeted datasets. This segmentation allows the system to benefit from large-scale pre-training while requiring minimal computational resources during deployment for subject-specific adaptation.
2Adaptability or versatility
If machine learning systems are trained on diverse object classes, then tracking versatility improves, but the system cannot be easily adjusted for specific individuals after deployment
Solution Approach 1:
The system transitions from a static pre-trained model to a dynamic adaptable model by implementing real-time fine-tuning capabilities. The neural network can dynamically adjust its parameters and weights during deployment to adapt to specific subjects while maintaining its general object detection versatility. This dynamic adaptation allows the system to serve multiple purposes across different subjects.
Solution Approach 2:
The system changes the parameters and weights of the pre-trained neural network during deployment to adapt to specific subjects. By modifying these parameters based on subject-specific data, the system maintains its versatile object detection capabilities while becoming specialized for tracking particular individuals. The parameter changes enable flexibility without requiring complete retraining.
3Productivity
If pre-trained neural networks are used for object detection, then general tracking capability is achieved, but real-time adjustment for specific subjects is not possible
Solution Approach 1:
The system performs self-service by automatically fine-tuning its own neural network parameters during deployment using subject-specific data. This self-adjustment capability allows the pre-trained model to adapt to specific subjects in real-time without requiring external retraining infrastructure, maintaining both speed and adaptability.
Solution Approach 2:
The system implements feedback loops where detection results are continuously evaluated and used to adjust neural network parameters. This feedback mechanism enables the pre-trained model to learn from actual subject-specific data during deployment, improving subject-specific tracking accuracy while maintaining the speed benefits of the pre-trained architecture.
Data Source
AI summary
A goal of the disclosure is to provide real-time adjustment of a deep learning-based tracking system to track a moving individual without using a labeled set of training data. Disclosed are systems and methods for tracking a moving individual with an autonomous drone. Initialization video data of the specific individual is obtained. Based on the initialization video data, real-time training of an input neural network is performed to generate a detection neural network that uniquely corresponds to the specific individual. Real-time video monitoring data of the specific individual and the surrounding environment is captured. Using the detection neural network, target detection is performed on the real-time video monitoring data and a detection output corresponding to a location of the specific individual within a given frame of the real-time video monitoring data is generated. Based on the detection output, first tracking commands are generated to maneuver and center the camera on the location of the specific individual.