Trip-Centered Video Storage for Multi-Camera Behavior Analysis
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Solution Overview
Problem
Current video storage methods are inefficient for behavior analysis, as they fail to effectively store and retrieve video segments containing track sequences of people or moving objects from multiple cameras, leading to data redundancy and difficulty in compacting video streams for analysis.
Innovation Solution
A method and system that capture and track video streams from multiple cameras, identify trip information including coordinates and temporal attributes, and create a trip-centered format by removing non-relevant segments, allowing for efficient storage and playback of trip sequences, enabling behavior analysis and annotation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If video streams from multiple cameras are stored in traditional formats, then complete video data is preserved, but data redundancy increases and storage efficiency decreases
Solution Approach 1:
The patent extracts and removes non-relevant video segments from multi-camera streams by identifying track sequences of people or moving objects. Only video segments containing these tracked objects are retained, eliminating redundant footage while preserving all behavior analysis-relevant data.
Solution Approach 2:
The patent segments video streams into discrete video segments based on track sequences. Each video segment corresponds to a specific time period where tracked objects are present, allowing for organized storage and efficient retrieval of behavior-related footage from multiple cameras.
2Loss of information
If all video segments from multiple cameras are retained, then no video information is lost, but retrieval and access complexity increases
Solution Approach 1:
The patent performs preliminary organization of video segments by creating a structured storage format that pre-arranges segments according to track sequences and camera identifiers. This preliminary structuring enables efficient retrieval without requiring complex search algorithms later, as the data is already organized by behavioral relevance.
3Reliability
If traditional video storage formats are used, then all video data is preserved, but behavior analysis efficiency decreases
Solution Approach 1:
The patent applies local quality by organizing video segments with specific metadata characteristics relevant to behavior analysis. Each video segment is tagged with track sequence information, camera identifiers, and temporal attributes, creating locally optimized data structures that enhance analysis efficiency while preserving overall data integrity.
Data Source
AI summary
The present invention is a method and system for storing videos by track sequences and selection of video segments in a manner to support “re-mining” by indexing and playback of individual visitors' entire trip to an area covered by overlapping cameras, allowing analysis and recognition of detailed behavior. The present invention captures video streams of the people in the area by multiple cameras and tracks the people in each of the video streams, producing track sequences in each video stream. Using the track sequences, the present invention finds trip information of the people. The present invention determines a first set of video segments that contain the trip information of the people, and compacts each of the video streams by removing a second set of video segments that do not contain the trip information of the people from each of the video streams. The video segments in the first set of video segments are associated with the people by indexing the video segments per person based on the trip information. The final storage format of the videos is a trip-centered format which sequences videos from across multiple cameras in a manner to facilitate multiple applications dealing with behavior analysis, and it is an efficient compact format without losing any video segments that contain the track sequences of the people.