Event-Driven Video Stitching for Multi-Camera Surveillance Tracking
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Solution Overview
Problem
Surveillance systems face challenges in efficiently stitching together video streams from multiple cameras to track and analyze events across non-overlapping viewpoints, leading to time-consuming and difficult event analysis due to the lack of integrated data.
Innovation Solution
A storage system with a controller configured to track objects across video streams, determine correlation indices, and create separate video streams based on these indices, storing only relevant frames in a memory, allowing for event-driven data stitching and improved event analysis.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If video streams from multiple cameras are stored and reviewed to track objects across non-overlapping viewpoints, then object tracking capability is improved, but time consumption for event analysis increases
Solution Approach 1:
The system performs preliminary object detection and tracking during video stream ingestion, identifying objects of interest before review. The controller tracks objects across video streams from multiple cameras and determines which video frames contain these objects, pre-processing the data so that event analysis can quickly access only relevant frames without manually reviewing entire video streams.
Solution Approach 2:
The system extracts and separates relevant video frames containing tracked objects from the complete video streams. The controller creates a curated video stream that includes only the video frames that contain the object of interest, removing unnecessary frames and camera views. This extraction process isolates the essential information needed for event analysis, dramatically reducing review time while maintaining complete object tracking capability.
2Loss of information
If complete video streams from all cameras are stored, then data completeness is improved, but storage requirements increase
Solution Approach 1:
The controller extracts only the essential data elements - video frames containing tracked objects - from the complete video streams. Instead of storing all frames from all cameras, the system identifies and extracts only those frames relevant to object tracking, creating a curated video stream that maintains data completeness for analysis purposes while significantly reducing the total volume of stored video data.
Solution Approach 2:
The system applies different quality levels to different portions of the video data based on relevance. Video frames containing tracked objects are preserved in high detail, while non-relevant frames are excluded from the curated stream. This local quality approach ensures that the most important data maintains full fidelity while reducing overall storage requirements by eliminating redundant or irrelevant content.
3Measurement precision
If all video frames from multiple cameras are reviewed to analyze events, then event detection accuracy is improved, but processing complexity increases
Solution Approach 1:
The controller performs preliminary processing by tracking objects across video streams and determining correlation indices before event analysis. This pre-processing step organizes video frames according to object presence and temporal relationships, creating a structured curated video stream that simplifies subsequent event detection while maintaining the ability to detect events with high accuracy across multiple camera viewpoints.
Data Source
AI summary
A storage system and method for event-driven data stitching in surveillance systems are provided. In one embodiment, a storage system is provided comprising a memory and a controller. The controller is configured to track an object in a plurality of video streams; determine which video frames in each of the plurality of video streams contain the object; create a separate video stream from the video frames that contain the object; and store the created separate video stream in the memory. Other embodiments are provided.