Prompt-Based Object Tracking With Third-Order Tensor Attention
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Solution Overview
Problem
Existing Multiple Object Tracking (MOT) methods are limited by the availability and diversity of object categories and annotations, and require manual selection or predefined object classes, lacking intuitiveness and responsiveness in tracking multiple objects based on textual descriptions.
Innovation Solution
A novel transformer-based eMbed-ENcoDE-extRact framework (MENDER) using third-order tensor decomposition for efficient attention, allowing adaptive tracking of multiple objects with textual descriptions, reducing computational complexity from cubic to quadratic scaling, and incorporating user input for intuitive tracking.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If third-order tensor decomposition is used for attention computation, then computational complexity is reduced from cubic to quadratic scaling, but the system complexity increases due to the need to integrate image, trajectory, and text modalities
Solution Approach 1:
The patent segments the attention computation into three independent modes (image-mode, trajectory-mode, and text-mode) corresponding to the three dimensions of the third-order tensor. Each mode can be computed separately and then combined, reducing the overall computational complexity from cubic to quadratic scaling while maintaining the integration of multiple modalities.
Solution Approach 2:
The patent transitions from traditional two-dimensional attention matrices to a third-order tensor structure that incorporates image, trajectory, and text dimensions. This dimensional expansion allows for more comprehensive multi-modal attention while the decomposition into three modes enables efficient computation by reducing the complexity from cubic to quadratic.
2Measurement precision
If multiple object tracking is performed with textual descriptions, then tracking accuracy and adaptability improve, but the time required for processing increases
Solution Approach 1:
The patent extracts visual features from image regions and computes attention weights in advance before final tracking decisions are made. By pre-computing the third-order tensor decomposition and attention matrices, the system reduces the time required for subsequent tracking operations while maintaining high accuracy in object identification and tracking.
3Adaptability or versatility
If class-agnostic tracking is implemented, then versatility and adaptability to diverse objects improve, but the difficulty of detecting and measuring object characteristics increases
Solution Approach 1:
The patent employs a universal third-order tensor framework that can handle multiple object classes and types through a single unified model. The tensor structure integrates image, trajectory, and text modalities, enabling the system to adapt to diverse objects without requiring class-specific processing, thus achieving class-agnostic tracking while maintaining the ability to detect various object characteristics.
Solution Approach 2:
The patent introduces text descriptions as an intermediary modality that bridges the gap between visual appearance and object identity. By incorporating text information into the third-order tensor, the system can infer object characteristics and identities more effectively, reducing the difficulty of detecting and measuring object properties in a class-agnostic manner.
Data Source
AI summary
Methods for tracking an object are disclosed. The method includes building a third-order tensor. The third-order tensor includes an image from a video, an object trajectory based, at least in part, on a previous image of the video, and text. The method further includes extracting a visual feature from an image region of the image and the object trajectory, determining an attention matrix based, at least in part, on at least one of the image region, the object trajectory, or the text, correlating the image region, the object trajectory, and the text, generating a context-aware object representation, incorporating the context-aware object representation with the visual feature, decoding an object bounding box and score from the context-aware object representation, tracking the object across at least one frame of the video, and predicting a trajectory of the object in the video.