Unified Neural Network for Object Detection and Tracking
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Solution Overview
Problem
Existing solutions for object detection, tracking, and classification in video image sequences struggle with accuracy and reliability at low image refresh rates, particularly when objects move quickly or are partially concealed.
Innovation Solution
A method that uses a single neural network calculation per time point to simultaneously detect, track, and classify objects in video frames, with a relative weighting between classification vectors and position features that adapts to the image refresh rate, ensuring reliable identification and tracking even at low frame rates.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional algorithms are used for object detection and tracking, then detection and tracking can be performed, but accuracy and reliability deteriorate at low image refresh rates
Solution Approach 1:
The patent combines detection, tracking, and classification into a single unified neural network model that processes video frames simultaneously. This integration allows the system to maintain reliable object tracking even at low frame rates by leveraging shared feature representations across all three tasks, rather than treating them as separate sequential operations.
Solution Approach 2:
The system dynamically adjusts the weighting between classification vectors and position features based on the time interval between frames. When frame intervals are large (low refresh rate), the system increases reliance on classification features to compensate for reduced positional information, thereby maintaining tracking reliability under varying temporal conditions.
2Productivity
If multiple separate calculations are performed for detection, tracking, and classification, then each task can be optimized independently, but computational complexity and processing time increase
Solution Approach 1:
The patent merges detection, tracking, and classification into a single neural network forward pass. The unified model shares convolutional feature extraction layers across all three tasks, performing simultaneous inference in one computation rather than three separate calculations. This significantly reduces processing time and computational complexity while maintaining the capability to perform all three functions.
3Speed
If objects move quickly relative to image refresh rate, then high speed detection is possible, but tracking accuracy and reidentification reliability deteriorate
Solution Approach 1:
The system dynamically weights the contribution of classification vectors versus position features based on object speed and frame interval. For fast-moving objects where position changes greatly between frames, the system increases reliance on classification features to maintain tracking precision, compensating for the limited positional information available at low refresh rates.
4Productivity
If a single neural network calculation is performed per time point, then processing efficiency improves, but the system must simultaneously perform detection, tracking, and classification
Solution Approach 1:
The neural network is designed as a universal model that performs detection, tracking, and classification through a single forward pass. The model generates multiple outputs (detection boxes, tracking IDs, and class predictions) from the same feature representations, making the single calculation serve multiple purposes simultaneously without requiring separate specialized models for each task.
Data Source
AI summary
A method for machine-based training of a computer-implemented network for common detecting, tracking, and classifying of at least one object in a video image sequence having a plurality of successive individual images. A combined error may be determined during the training, which error results from the errors of the determining of the class identification vector, determining of the at least one identification vector, the determining of the specific bounding box regression, and the determining of the inter-frame regression.