Multi-time Search Analytics for Smart Video Indexing
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Solution Overview
Problem
Current CCTV surveillance systems face inefficiencies due to high computational costs, large file sizes, and complexity in video indexing and playback, leading to time-consuming analysis and potential omissions in monitoring, with existing systems like BRIEFCAM not addressing multi-time search analytics effectively.
Innovation Solution
A multi-time search analytics system that operates with a single operator, supports various subcomponents, and uses advanced filters for real-time monitoring, allowing simultaneous playback and filtering of video recordings across multiple platforms, with a focus on efficient indexing and minimal processor load, enabling nanosecond precision and compatibility with different file formats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If continuous video recordings are stored in current CCTV systems, then complete surveillance coverage is achieved, but file size becomes remarkably large causing storage difficulties and requiring deletion of old files
Solution Approach 1:
The patent extracts only the essential information from continuous video recordings by detecting and indexing key frames based on motion detection and frame difference analysis. Instead of storing all video data, only representative key frames are saved, dramatically reducing storage requirements while maintaining surveillance effectiveness.
Solution Approach 2:
The system applies different processing quality levels to different parts of the video data. High-quality storage is applied only to key frames that contain important information, while non-key frames are either compressed more aggressively or not stored at all, optimizing the balance between storage efficiency and surveillance quality.
2Device complexity
If all video recordings are kept on a single file as in prior art systems, then simple storage structure is maintained, but file transfer becomes complex and wasteful due to large file size
Solution Approach 1:
The patent divides the video recording into multiple independent segments or files based on time periods, camera sources, or event types. Each segment contains only relevant key frames rather than all video data, making individual files smaller and more efficient to transfer and manage while maintaining an organized storage structure.
3Measurement precision
If frame differences and color changes are monitored during recording as in BRIEFCAM system, then video indexing is achieved, but processor power is remarkably wasted
Solution Approach 1:
The system performs frame difference and color change monitoring at periodic intervals rather than continuously analyzing every frame. Motion detection triggers key frame identification only when changes exceed certain thresholds, reducing processor workload while maintaining adequate indexing accuracy for surveillance purposes.
4Manufacturing precision
If video playback is performed in current systems, then complete video analysis is possible, but it takes a long time due to excessive video length
Solution Approach 1:
The system extracts and indexes only key frames that represent important moments in the video sequence. During playback and analysis, operators review only these extracted key frames rather than watching entire hours of continuous video, dramatically reducing analysis time while maintaining the ability to detect and investigate important events.
Data Source
AI summary
Multi-time search analytics for smart video indexing based on active search and video database, which is configured to be operated without any need for a second broadcast with a camera, a second large data index, additional servers, a third additional software for indexing and analytics, a third additional face recognition system, a license recognition system, an object recognition system, or an additional net bandwidth for a new hardware. The method of operating multi-time search analytics for the additional net bandwidth for new hardware includes following steps: recognizing objects, activating software components and/or incoming alarms, collecting the alarms in an alarm database pool, extracting a first video with a same length in the alarm database pool, performing a motion recognition with H264/H265, recording detected motions by a video content recorder, transferring the detected motions to a timeline adjuster and changing the detected motions in accordance with an Alpha Time Zone.