Building Graph Re-Identification for Multi-Camera Video Stitching
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Solution Overview
Problem
Existing surveillance systems face inefficiencies in video stitching due to resource intensity, time consumption, and the need to review extensive video clips, often overlooking important details, and are costly with overlapping camera placements, and handle varying camera resolutions, frame rates, and angles.
Innovation Solution
A building system uses a building graph to track entity movement by stitching videos from multiple cameras, generating a trajectory graph, and normalizing based on camera characteristics, with AI-driven digital twins to extrapolate paths and integrate audio, photo, and geolocation data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional video stitching methods are used to track entity movement across multiple cameras, then complete tracking paths can be obtained, but the process becomes extremely resource intensive and time consuming requiring review of hundreds of hours of surveillance video
Solution Approach 1:
The system performs preliminary actions by pre-processing video clips to extract entity detections and metadata (timestamps, locations, camera IDs) before stitching is needed. This allows the system to have ready-to-use structured data when a tracking request occurs, eliminating the need to review raw video during the actual stitching process.
Solution Approach 2:
The patent introduces an intermediary data structure (metadata containing entity detections with timestamps, locations, and camera identifiers) that mediates between raw video clips and the final stitched output. This intermediary layer allows automated matching of entity appearances across cameras without human reviewers watching video content.
2Reliability
If cameras are placed throughout the building to ensure overlapping coverage for tracking, then complete entity paths can be captured, but the system becomes expensive to deploy and maintain
Solution Approach 1:
The system uses partial action by selectively processing only those video clips and metadata that contain entity detections relevant to the tracking request, rather than requiring complete overlapping coverage from all cameras. The graph-based approach identifies and processes only the necessary camera segments along the entity's path.
Solution Approach 2:
The patent changes the parameter of camera coverage from requiring spatial overlap to requiring temporal and logical connectivity through graph relationships. Cameras don't need to physically overlap their fields of view, but rather need to be connected through the building's spatial graph structure, allowing tracking across non-overlapping camera zones.
3Reliability
If videos from multiple cameras with different resolutions, frame rates, and angles are stitched together, then comprehensive tracking is achieved, but the video quality and consistency deteriorate
Solution Approach 1:
The patent segments the tracking video into distinct camera-originated clips rather than attempting to create a seamless unified video. Each clip retains its original camera's resolution, frame rate, and angle characteristics, with clear transitions between clips. This segmentation preserves individual video quality while achieving comprehensive tracking coverage.
Data Source
AI summary
A building system can operate to receive a request to generate a video tracking movement of an entity throughout the building. The building system can operate to search, based on a building graph, a database for a set of images or videos of cameras of the building that track the entity throughout the building. The building graph can include nodes indicating spaces of the building and the cameras of the building. The building graph can include building graph including edges between the nodes representing relationships between the spaces and the cameras. The building system can operate to join the set of images or videos together to create the video.