Video Query by Example System with Tree-Based Indexing
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Solution Overview
Problem
Large video surveillance systems face slow response times due to the extensive processing required for analyzing video data, overwhelming operators during forensic analyses in environments like airports and casinos.
Innovation Solution
A video query by example system with a video analytics component, appearance model generation, and a searching and matching engine, utilizing a tree-based index for fast search capabilities, allowing for efficient identification and tracking of objects across multiple cameras.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional video analysis processing is used, then comprehensive object detection and analysis can be achieved, but response time becomes slow due to extensive processing requirements
Solution Approach 1:
The patent segments the video analysis process into distinct components: motion detection identifies moving regions, object detection processes those regions to identify objects, and appearance model generation creates templates. This segmentation allows parallel processing and optimization of each stage, reducing overall response time while maintaining detection accuracy.
Solution Approach 2:
The system performs preliminary actions by pre-generating appearance models for detected objects and pre-processing video frames to extract key features before full analysis. Motion detection and initial object identification occur in advance, allowing the system to prepare data structures and reduce computational load during the actual search and matching phases.
2Measurement precision
If comprehensive video data analysis is performed, then accurate object identification is achieved, but processing complexity increases
Solution Approach 1:
The patent applies local quality by focusing computational resources on specific regions of interest rather than processing entire video frames uniformly. Motion detection identifies active regions, and object detection concentrates analysis only on those regions, allocating processing power locally where needed rather than globally across the entire frame, thus reducing overall complexity.
Solution Approach 2:
The system performs partial action by implementing multiple detection stages (motion detection, object detection, appearance matching) where each stage processes only a subset of the data. Not all video data undergoes full analysis - only regions with motion and detected objects receive intensive processing, reducing total computational complexity while maintaining identification accuracy.
Data Source
AI summary
A system includes a motion detection processor, a motion tracking processor, a people detection processor, a controller, a fusion processor, an appearance model generator processor, a database, a fast search processor, and a matching processor. The motion detection processor, the motion tracking processor, the controller, the people detection processor, the fusion processor, and the appearance model generator processor comprise an analytics pipeline, and the database and the fast search processor comprise a data index pipeline.