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Localizing video moments based on the movement patterns of objects is an important task in video analytics. Existing video analytics systems offer two types of querying interfaces based on natural language and SQL, respectively. However, both types of interfaces have major limitations. SQL-based systems require high query specification time, whereas natural language-based systems require large training datasets to achieve satisfactory retrieval accuracy. To address these limitations, we present SketchQL, a video database management system (VDBMS) for offline, exploratory video moment retrieval that is both easy to use and generalizes well across multiple video moment datasets. To improve ease-of-use, SketchQL features avisual query interfacethat enables users to sketch complex visual queries through intuitive drag-and-drop actions. To improve generalizability, SketchQL operates on object-tracking primitives that are reliably extracted across various datasets using pre-trained models. We present a learned similarity search algorithm for retrieving video moments closely matching the user's visual query based on object trajectories. SketchQL trains the model on a diverse dataset generated with a novel simulator, that enhances its accuracy across a wide array of datasets and queries. We evaluate SketchQL on four real-world datasets with nine queries, demonstrating its superior usability and retrieval accuracy over state-of-the-art VDBMSs.
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This content will become publicly available on May 1, 2027
Demonstration of WayPoint: Interactive Natural Language Querying for Spatio-Temporal Video Events
Natural language (NL) provides an intuitive interface for querying video datasets. However, existing approaches that use Multimodal Large Language Models to directly map NL queries to video results offer limited feedback mechanisms beyond prompt engineering. We present WayPoint, a prototype system for NL-based querying of spatio-temporal video events through a user-in-the-loop approach. WayPoint translates NL queries into a keyframe-inspired intermediate representation that enables fuzzy matching of spatio-temporal constraints. This structured representation allows users to refine query semantics through natural language feedback that is applied to the query logic and has deterministic effects on execution. Users can also tune retrieval performance by providing feedback on retrieved results. We demonstrate WayPoint's query synthesis and retrieval refinement workflows on the nuScenes dataset.
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- Award ID(s):
- 2335881
- PAR ID:
- 10673628
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 979-8-4007-2450-3
- Subject(s) / Keyword(s):
- Natural Language Interfaces Video Querying Human-in-the-loop
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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