Joint Scene Segmentation and One-Shot Long-Term Object Tracking
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Solution Overview
Problem
Existing solutions for scene segmentation and object tracking have limitations, such as relying on local searches for short-term tracking and being limited by pre-defined classes in object detection, which makes them unsuitable for long-term tracking and efficient multi-object tracking.
Innovation Solution
The proposed system performs joint scene segmentation and one-shot long-term object tracking by leveraging features from semantic scene segmentation to track objects over multiple frames, using a method that includes determining features from frames, generating masks, and updating a feature memory for efficient tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If local search methods are used for object tracking, then short-term tracking performance is improved, but long-term tracking capability deteriorates
Solution Approach 1:
The patent segments the tracking process into initialization phase (using detection and mask generation) and tracking phase (using feature matching and representation comparison). This segmentation allows different methods to be used at different stages, enabling both precision and long-term capability
Solution Approach 2:
The patent performs preliminary actions by generating a representation of the target object (foreground-background representation) during initialization. This pre-computed representation serves as a reference for long-term tracking, enabling the system to track objects beyond the immediate local search window
2Productivity
If pre-defined classes are used in object detection, then detection speed is improved, but adaptability to new objects deteriorates
Solution Approach 1:
The patent creates a copy of the target object's appearance and spatial information in the form of a foreground-background representation. This copy serves as a template that can be compared against future frames, enabling the system to track objects without requiring pre-defined class labels and maintaining high detection speed
Solution Approach 2:
The foreground-background representation serves multiple functions: it acts as a detection template for new objects, a tracking reference for long-term tracking, and a segmentation mask for scene understanding. This multi-functionality provides both speed and adaptability without requiring separate mechanisms
3Reliability
If separate detection and tracking systems are used, then tracking reliability is improved, but computational complexity deteriorates
Solution Approach 1:
The patent merges detection and tracking into a unified system where the same neural network generates both detection results and foreground-background representations. The tracking process uses these representations directly without requiring separate detection runs, reducing computational complexity while maintaining reliability through consistent feature extraction
Data Source
AI summary
Systems and techniques are provided for performing scene segmentation and object tracking. For example, a method for processing one or more frames. The method may include determining first one or more features from a first frame. The first frame includes a target object. The method may include obtaining a first mask associated with the first frame. The first mask includes an indication of the target object. The method may further include generating, based on the first mask and the first one or more features, a representation of a foreground and a background of the first frame. The method may include determining second one or more features from a second frame and determining, based on the representation of the foreground and the background of the first frame and the second one or more features, a location of the target object in the second frame.