Multimodal Aerial Object Tracking With Click-Language Grounding
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Solution Overview
Problem
Current single object tracking (SOT) techniques face challenges in aerial video data due to motion blur, rapid camera movement, lighting and weather conditions, and lack of language-annotated data, particularly for small objects with low resolution and occlusions, leading to ambiguity and inefficiency in tracking.
Innovation Solution
A SOT pipeline that integrates a click modality with vision and language cues, using a unified fusion encoder, click and vision memory modules, and a unified fusion decoder to enhance target localization and tracking efficiency, especially for small objects.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If natural language description is used to identify target object, then target semantics accuracy is improved, but annotation difficulty increases for small objects
Solution Approach 1:
The system segments the target identification process into two distinct input modalities: natural language description for semantic specification and point input for precise location grounding. This segmentation allows each modality to handle its strength independently, with language providing semantic accuracy and points providing annotation ease, resolving the contradiction between semantics accuracy and annotation difficulty.
Solution Approach 2:
The system introduces point inputs as an intermediary mechanism that bridges the gap between natural language descriptions and precise object localization. The point input serves as a grounding signal that anchors the semantic meaning of the language description to a specific location in the image, making annotation more straightforward while maintaining semantic accuracy.
2Ease of manufacture
If bounding box is used to specify target, then annotation simplicity is improved, but target semantics accuracy deteriorates
Solution Approach 1:
The system replaces the bounding box intermediary with a point input intermediary. The point input provides a more precise semantic anchor for the target object while maintaining annotation simplicity. A single point is easier to annotate than a bounding box and provides better semantic grounding for small objects, resolving the contradiction between annotation simplicity and semantics accuracy.
3Adaptability or versatility
If aerial vehicle captures object from all directions, then tracking coverage is improved, but object appearance consistency deteriorates
Solution Approach 1:
The system employs feedback mechanisms through memory modules that store historical semantic appearances and click information. This feedback allows the tracker to maintain consistent object representation despite changes in appearance due to aerial vehicle movement and multiple viewing angles, resolving the contradiction between tracking coverage and appearance consistency.
Solution Approach 2:
The system performs preliminary encoding of semantic information from language descriptions and point inputs before tracking begins. This preliminary action creates a robust semantic representation that remains stable throughout the tracking sequence, allowing the system to handle appearance variations while maintaining consistent object identification across different viewing angles.
Data Source
AI summary
A data processing system implements obtaining a first frame of video content comprising a plurality of frames over which a target object is to be tracked; obtaining a first point input denoting a point on the first frame of video content representing a location of the target object on the first frame of video content; obtaining a natural language description of the target object; encoding the first frame of video content, the first point input, and the natural language description of the target object as fused encoding information using a single object tracking pipeline; and tracking the target object with the single object tracking pipeline using the fused encoding information.