Neural Network Merging Object Detection and Feature Extraction
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Solution Overview
Problem
Neural networks used for video analytics face challenges in real-time object tracking in scenes with multiple moving objects, as they require separate rounds of convolution filters for object detection and feature extraction, leading to increased computational latency and inability to track objects accurately at high frame rates.
Innovation Solution
A neural network system that merges object detection and feature extraction into a single round of convolution filters, using a Siamese training architecture and contrastive loss to determine filters and parameters, allowing for real-time video tracking without latency issues due to the number of objects in the scene.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If separate rounds of convolution filters are used for object detection and feature extraction, then detection accuracy is improved, but computational latency increases
Solution Approach 1:
The patent combines object detection and feature extraction into a single unified convolutional neural network architecture. The network performs both detection and feature extraction simultaneously through integrated convolutional layers, eliminating the need for separate processing rounds and reducing computational latency while maintaining detection accuracy.
Solution Approach 2:
The convolutional neural network is designed with multi-functional capabilities to perform both object detection and feature extraction using the same set of convolutional filters. This universal architecture allows the system to achieve multiple objectives in a single processing pass, improving efficiency without sacrificing performance.
2Measurement precision
If multiple convolution rounds are used for accurate feature extraction, then feature determination accuracy is improved, but processing speed decreases
Solution Approach 1:
The patent merges multiple convolution rounds into a single integrated convolutional layer structure that performs detection and feature extraction concurrently. This consolidation reduces the number of sequential processing steps while maintaining the computational depth needed for accurate feature determination, thereby improving processing speed.
Solution Approach 2:
The unified neural network architecture enables continuous processing where detection and feature extraction occur in an uninterrupted single pass through the convolutional layers. This eliminates idle time between separate processing rounds and maintains continuous useful action, improving overall processing throughput.
3Reliability
If separate convolution layers are used for detection and feature extraction, then object tracking accuracy is improved, but real-time processing capability is lost
Solution Approach 1:
The patent integrates detection and feature extraction into a single convolutional processing pipeline that operates in real-time. This unified architecture processes video frames at high frame rates by eliminating the sequential overhead of separate processing stages, enabling real-time object tracking while maintaining accuracy.
Solution Approach 2:
The neural network architecture is designed to be dynamically efficient, adapting to real-time processing requirements through its integrated structure. The single-pass convolutional approach allows the system to maintain high processing speeds variable with frame rate requirements while preserving tracking accuracy through the unified feature extraction pathway.
Data Source
AI summary
Examples include detecting objects and determining a set of features for the objects. Examples include receiving a first image input, generating a number of feature maps from the first image input using a number of convolution filters, generating a first number of fully connected layers directly based on the number of feature maps, and detecting a number of objects in the first image and determining a set of features for each object from the first number of fully connected layers.