Differential Frame Neural Tracking for Lower-Cost Computer Vision
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Solution Overview
Problem
Existing neural network-based object tracking techniques are computationally expensive due to the need for separate networks for detection and tracking, and inefficient use of temporally correlated information between frames.
Innovation Solution
Utilizing differential images computed between consecutive frames as input to a neural network, with preprocessing techniques to enhance signal-to-noise ratio and selective processing of useful information, and feature fusion of differential and original frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If separate neural networks are used for object detection and tracking, then tracking accuracy is improved, but computational cost increases
Solution Approach 1:
The patent combines object detection and tracking into a single neural network model that processes differential images. The unified architecture eliminates the need for separate detection and tracking networks while maintaining tracking accuracy through the integration of temporal information processing capabilities.
Solution Approach 2:
The neural network is designed to perform multiple functions - both object detection and tracking - within a single unified model. The network processes differential images to simultaneously identify objects and track their motion across frames, making the system more computationally efficient while maintaining comprehensive functionality.
2Ease of operation
If each frame is processed individually, then processing simplicity is maintained, but temporal information utilization is reduced
Solution Approach 1:
The system performs preliminary computation by calculating differential images between consecutive frames before feeding data to the neural network. This preliminary processing extracts temporal information and motion cues in advance, allowing the network to process frames more efficiently while fully utilizing temporal relationships.
Solution Approach 2:
Differential images serve as an intermediary representation that captures temporal relationships between frames. Instead of processing raw frames individually, the network processes these intermediate differential representations that encode motion and change information, thereby utilizing temporal information without complicating the overall processing architecture.
3Loss of information
If all frame information is processed, then complete information is available, but computational efficiency decreases
Solution Approach 1:
The system extracts only the relevant temporal information by computing differential images that highlight changes between frames. This extraction process removes redundant static information and focuses computational resources on processing only the dynamic, temporally-correlated information that is essential for tracking tasks.
Solution Approach 2:
The transformation from raw frame data to differential image representations changes the data parameters to emphasize temporal variations. This parameter transformation consolidates information across multiple frames into a more compact representation that maintains completeness while reducing computational requirements.
Data Source
AI summary
Systems and methods are described for performing a computer vision task on a sequence of frames. A first frame and a second frame are obtained, corresponding to a first timestep and a second timestep, respectively, in a sequence of frames. A differential image is computed between the first frame and the second frame. A predicted output is generated by forward propagating the differential image through a neural network that is trained to perform a computer vision task.