Video Instance Segmentation With Query Embedding Tracking
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Solution Overview
Problem
Performing video instance segmentation in videos with multiple objects requires significant computing resources, including memory and time, which can be improved.
Innovation Solution
A minimal video instance segmentation (MinVIS) framework that utilizes a query-based image instance segmentation model to process frames independently, applying bipartite matching of query embeddings for tracking objects without video-based training or manual heuristics, allowing for efficient segmentation and tracking of objects across frames.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If video-based architectures or training procedures are used for video instance segmentation, then tracking accuracy across frames is improved, but computational resources and memory usage increase significantly
Solution Approach 1:
The patent segments the video processing task into independent frame-level instance segmentation operations. Instead of processing the entire video sequence as a unified temporal problem, each frame is processed independently using image instance segmentation models, eliminating the need for video-based architectures while maintaining tracking capability through query embedding association across frames.
Solution Approach 2:
The patent uses query embeddings generated from image instance segmentation models as reusable representations that can be associated across frames. These query embeddings serve as compact copies of object information that can be matched and tracked without requiring full video-based processing, reducing computational overhead while preserving tracking accuracy.
2Stability of the object's composition
If video-based architectures are used for instance segmentation, then temporal consistency is improved, but device complexity increases
Solution Approach 1:
The patent divides the complex video instance segmentation problem into simpler, independent frame-level segmentation tasks. Each frame is processed by standard image instance segmentation models without requiring specialized video-based architectures, thereby reducing device complexity while maintaining temporal consistency through post-processing association of query embeddings.
Solution Approach 2:
The patent introduces query embeddings as an intermediary representation that bridges independent frame-level segmentations. These query embeddings serve as mediators that enable temporal association and tracking across frames without requiring direct video-based architectural connections, simplifying the overall system complexity.
3Reliability
If manual heuristics are applied for object tracking, then tracking performance is improved, but ease of operation deteriorates due to manual intervention requirements
Solution Approach 1:
The patent enables the system to automatically associate and track objects across frames through learned query embedding matching without requiring manual heuristics or human intervention. The model self-services the tracking function by leveraging the semantic information encoded in query embeddings, achieving both high tracking performance and full automation.
Solution Approach 2:
The patent replaces manual heuristic-based tracking mechanisms with a learned, data-driven approach using query embedding association. This substitution eliminates the need for hand-crafted tracking rules and manual interventions, achieving automated tracking performance through machine learning-based query matching.
Data Source
AI summary
Apparatuses, systems, and techniques to track one or more objects in one or more frames of a video. In at least one embodiment, one or more objects in one or more frames of a video are tracked based on, for example, one or more sets of embeddings.