Inference Engine Video Analytics Metadata Event Detection
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Solution Overview
Problem
Current video data management and forensic search techniques in video surveillance applications are insufficient in terms of effectiveness, extendibility, and flexibility, struggling to efficiently process and analyze large volumes of video data for event detection and behavior analysis.
Innovation Solution
The implementation of an inference engine-based system that allows users to specify search criteria, analyze video data for events or behaviors, and perform actions upon successful detection, utilizing a hierarchical pyramid structure, logic trees, and filters to identify and combine behavior events, and generate actions such as logging, notifications, or statistics reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional video processing systems are used for forensic search and data mining, then basic video analysis can be performed, but the systems are insufficient in terms of effectiveness, extendibility and flexibility when processing large volumes of video data for complex event detection
Solution Approach 1:
The system segments video data processing into distinct modular components: video data reception module, metadata generation module, inference engine module, and action execution module. Each module handles specific tasks independently, allowing the system to process complex video analytics through organized, manageable segments that can be independently optimized and extended.
Solution Approach 2:
The inference engine dynamically evaluates video analytics metadata against user-specified search criteria and adjusts processing based on detected events. The system adapts its behavior by performing different actions (logging, notifications, statistics reporting) based on real-time event detection, enabling flexible response to varying video content and search requirements.
2Measurement precision
If comprehensive video data analysis is performed to detect complex events and behaviors, then detection accuracy improves, but computing power demand and processing time increase significantly
Solution Approach 1:
The system generates video analytics metadata in advance during video processing, organizing data about objects, behaviors, and events before final search queries are executed. This preliminary structuring of video data into standardized metadata formats enables faster, more efficient querying without requiring intensive computing power during the actual search and detection phases.
3Adaptability or versatility
If detailed video analytics metadata is generated and processed, then event detection capability improves, but data storage space requirements increase
Solution Approach 1:
The system extracts only the essential and relevant features from video data into structured metadata, rather than storing and processing complete video streams or all possible video attributes. By taking out only the critical information needed for event detection (object properties, behaviors, temporal relationships), the system maintains high detection capability while minimizing storage requirements.
4Measurement precision
If multiple search criteria and complex inference rules are implemented, then search precision improves, but system complexity increases
Solution Approach 1:
The inference engine is designed as a universal platform that handles multiple search criteria, inference rules, and event detection tasks through a single integrated system. The engine can evaluate various types of search conditions (object properties, behaviors, temporal patterns) using a unified metadata structure and inference mechanism, reducing overall system complexity compared to having separate specialized systems for each search type.
Data Source
AI summary
Embodiments of the disclosure provide for systems and methods for searching video data for events and/or behaviors. An inference engine can be used to aide in the searching. In some embodiments, a user can specify various search criteria, for example, a video source(s), an event(s) or behavior(s) to search, and an action(s) to perform in the event of a successful search. The search can be performed by analyzing an object(s) found within scenes of the video data. An object can be identified by a number of attributes specified by the user. Once the search criteria has been received from the user, the video data can be received (or extracted from storage), the data analyzed for the specified events (or behaviors), and the specified action performed in the event a successful search occurs.