Optical Flow Localization for Detecting Novel Objects in Video
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Solution Overview
Problem
Existing object localization systems fail to accurately identify 'novel' objects, which are objects not recognized by the known systems due to rarity, unusual appearance, or different visual characteristics.
Innovation Solution
A novel object localization system is trained using optical flow object localization data to identify objects based on movement, complementing known systems that rely on visual appearance, thereby generating training data to improve the accuracy of known systems.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a known object localization system is used to identify objects based on visual appearance, then objects with typical visual characteristics can be detected, but novel objects with unusual appearance or rarity cannot be accurately identified
Solution Approach 1:
The patent transitions from analyzing static visual appearance (2D image data) to incorporating temporal motion information (optical flow data across video frames). By adding the time dimension and motion characteristics, the system can detect novel objects that differ visually from training data but exhibit distinctive movement patterns, thereby resolving the contradiction between accuracy for typical objects and adaptability to novel objects
2Reliability
If an object localization system is trained on static images, then visual appearance features can be learned, but movement-based identification capabilities are lost
Solution Approach 1:
The system pre-computes optical flow fields from video sequences before object detection. By preparing motion information in advance through optical flow calculation, the system preserves movement characteristics that would otherwise be lost, while maintaining the reliability of detection through consistent feature extraction across frames
3Adaptability or versatility
If optical flow processing is applied to every video frame, then movement-based object detection can be achieved, but computational complexity increases
Solution Approach 1:
The system applies optical flow processing selectively rather than uniformly to all frames. By computing optical flow only where needed (e.g., in regions with detected motion or for specific novel object candidates), the system achieves novel object detection capability while avoiding the excessive computational burden of processing every frame completely
Data Source
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AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for training an optical flow object localization system and a novel object localization system. In a first aspect, the optical flow object localization system is trained to process an optical flow image to generate object localization data defining locations of objects depicted in a video frame corresponding to the optical flow image. In a second aspect, a novel object localization system is trained to process a video frame to generate object localization data defining locations of novel objects depicted in the video frame.